WO2019018315A1 - Alignement de données de signal mesurées avec des données de localisation slam et utilisations associées - Google Patents

Alignement de données de signal mesurées avec des données de localisation slam et utilisations associées Download PDF

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Publication number
WO2019018315A1
WO2019018315A1 PCT/US2018/042346 US2018042346W WO2019018315A1 WO 2019018315 A1 WO2019018315 A1 WO 2019018315A1 US 2018042346 W US2018042346 W US 2018042346W WO 2019018315 A1 WO2019018315 A1 WO 2019018315A1
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WIPO (PCT)
Prior art keywords
sensor
map
sensor readings
spatial
environment
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PCT/US2018/042346
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English (en)
Inventor
Ji Zhang
Kevin Joseph DOWLING
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Kaarta, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Priority claimed from PCT/US2018/015403 external-priority patent/WO2018140701A1/fr
Priority claimed from PCT/US2018/040269 external-priority patent/WO2019006289A1/fr
Application filed by Kaarta, Inc. filed Critical Kaarta, Inc.
Priority to EP18834521.9A priority Critical patent/EP3656138A4/fr
Publication of WO2019018315A1 publication Critical patent/WO2019018315A1/fr
Priority to US16/745,775 priority patent/US10989542B2/en
Priority to US17/202,602 priority patent/US11506500B2/en
Priority to US17/964,307 priority patent/US20230288209A1/en

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G01C21/1652—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with ranging devices, e.g. LIDAR or RADAR
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G01C21/1656—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with passive imaging devices, e.g. cameras
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/87—Combinations of systems using electromagnetic waves other than radio waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/16—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using electromagnetic waves other than radio waves
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Definitions

  • PCT Application No. PCT/US 18/40269 (Atty. Dckt. No. KRTA-0014-WO) claims priority to, and is a continuation-in-part of, PCT Application No. PCT/US2018/015403 (Atty. Dckt. No. KRTA-0010-WO) entitled "LASER SCANNER WITH REAL-TIME, ONLINE EGO-MOTION ESTIMATION,” filed on January 26, 2018.
  • PCT Application No. PCT/US2018/015403 claims priority to, and is a continuation-in-part of, PCT Application No.
  • PCT Application No. PCT/US2017/055938 (Atty. Dckt. No. KRTA-0008-WO) claims priority to, and is a continuation-in-part of, PCT Application No.
  • PCT Application No. PCT/US2018/015403 claims priority to PCT Application No. PCT/US2017/021120 (Atty. Dckt. No. KRTA-0005- WO).
  • PCT Application No. PCT/US2017/055938 (Atty. Dckt. No. KRTA-0008-WO) further claims priority to U.S. Provisional No. 62/406,910 (Atty. Dckt. No. KRTA-0002- P02), entitled "LASER SCANNER WITH REAL-TIME, ONLINE EGO-MOTION
  • a primary means of finding outdoor location are satellite-based radio navigation systems such as the Global Positioning System (GPS) or similar systems such as GLONASS, Galileo, Baidu Navigation Satellite System, and NAVIC. These systems are constellations of satellites that use radio-navigation to provide position on or near the surface of the Earth. However due to attenuation by walls, roofs, and structures determining position is poor or non-existent in indoor spaces. Thus, for indoor navigation other methods have been proposed and implemented using infrastructure, self-estimates such as odometry, and external sources such as passive (barcodes) or active beacons (RF, IR, etc).
  • Position information can be used in many applications to find a particular location and providing a path to get to/from that location.
  • a method comprises retrieving a map of a 3D geometry of an environment the map comprising a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes and matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.
  • a system comprises a device comprising at least one sensor the device adapted to receive a plurality of sensor readings within an environment wherein each of the sensor readings corresponds to at least one non-spatial attribute and a processor adapted to retrieve a map of a 3D geometry of an environment the map comprising a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment and further adapted to match the plurality of received sensor readings to at least one location in the map to produce a determined sensor location.
  • a method comprises accessing a map comprising a point cloud of an environment comprising a plurality of non-spatial attribute values, receiving, for each of a plurality of non-spatial attributes, a plurality of non-spatial sensor readings within the environment and referencing the received plurality of non-spatial sensor readings to the map resulting in a referenced heat map.
  • a system comprises a device adapted to produce a map comprising a point cloud of an environment comprising a plurality of non-spatial attribute values and further adapted to measure, for each of a plurality of non-spatial attributes, a plurality of non-spatial sensor readings and a processor adapted to reference the measured plurality of non-spatial sensor readings to the map resulting in a referenced heat map.
  • a method comprises accessing a map of a 3D geometry of an environment the map comprising a plurality of non-spatial attribute values each corresponding to a non-spatial attribute and indicative of a plurality of non-spatial sensor readings acquired throughout the environment from a plurality of signal emitters, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to the non- spatial attribute, creating a heat map of the sensor readings referenced to the map and generating a recommended position of at least one signal emitter based, at least in part, on the heat map.
  • a system comprises a device adapted to produce a map of a 3D geometry of an environment the map comprising a plurality of non-spatial attribute values each corresponding to a non-spatial attribute and indicative of a plurality of non-spatial sensor readings acquired throughout the environment from a plurality of signal emitters and receive a plurality of sensor readings within the environment wherein each of the sensor readings corresponds to the non- spatial attribute and a processor adapted to create a heat map of the sensor readings referenced to the map and generate a recommended position of at least one signal emitter based, at least in part, on the heat map.
  • a method comprises retrieving a map of a 3D geometry of an environment the map comprising a plurality of non-spatial attribute values each corresponding to one of a plurality of non-spatial attributes and indicative of a plurality of non-spatial sensor readings acquired throughout the environment, receiving a plurality of sensor readings from a device within the environment wherein each of the sensor readings corresponds to at least one of the non-spatial attributes, matching the plurality of received sensor readings to at least one location in the map to produce a determined sensor location, retrieving additional sensor data from the determined sensor location and comparing the additional sensor data to data derived from the map.
  • a system comprises a device adapted to produce a map of a 3D geometry of an environment the map comprising a plurality of non-spatial attribute values each corresponding to a non-spatial attribute and indicative of a plurality of non-spatial sensor readings acquired throughout the environment from a plurality of signal emitters and receive a plurality of sensor readings within the environment wherein each of the sensor readings corresponds to the non- spatial attribute and a processor adapted to match the plurality of received sensor readings to at least one location in the map to produce a determined sensor location and retrieving additional sensor data from the determined sensor location and compare the additional sensor data to data derived from the map.
  • FIG. 1 illustrates a block diagram of an embodiment of a mapping system.
  • FIG. 2 illustrates an embodiment a block diagram of the three computational modules and their respective feedback features of the mapping system of FIG. 1.
  • FIG. 3 illustrates an embodiment of a Kalmann filter model for refining positional information into a map.
  • FIG. 4 illustrates an embodiment of a factor graph optimization model for refining positional information into a map.
  • FIG. 5 illustrates an embodiment of a visual-inertial odometry subsystem.
  • FIG. 6 illustrates an embodiment of a scan matching subsystem.
  • FIG. 7A illustrates an embodiment of a large area map having coarse detail resolution.
  • FIG. 7B illustrates an embodiment of a small area map having fine detail resolution.
  • FIG. 8A illustrates an embodiment of multi-thread scan matching.
  • FIG. 8B illustrates an embodiment of single-thread scan matching.
  • FIG. 9A illustrates an embodiment of a block diagram of the three computational modules in which feedback data from the visual-inertial odometry unit is suppressed due to data degradation.
  • FIG. 9B illustrates an embodiment of the three computational modules in which feedback data from the scan matching unit is suppressed due to data degradation.
  • FIG. 10 illustrates an embodiment of the three computational modules in which feedback data from the visual-inertial odometry unit and the scan matching unit are partially suppressed due to data degradation.
  • FIG. 11 illustrates an embodiment of estimated trajectories of a mobile mapping device.
  • FIG. 12 illustrates bidirectional information flow according to an exemplary and non-limiting embodiment.
  • FIGS. 13a and 13b illustrate a dynamically reconfigurable system according to an exemplary and non-limiting embodiment.
  • FIG. 14 illustrates priority feedback for IMU bias correction according to an exemplary and non-limiting embodiment.
  • FIGS. 15a and 15b illustrate a two-layer voxel representation of a map according to an exemplary and non-limiting embodiment.
  • FIGS. 16a and 16b illustrate multi-thread processing of scan matching according to an exemplary and non-limiting embodiment.
  • FIGS. 17a and 17b illustrate exemplary and non-limiting embodiments of a SLAM system.
  • FIG. 18 illustrates an exemplary and non-limiting embodiment of a SLAM enclosure.
  • FIGS. 19a, 19b and 19c illustrate exemplary and non-limiting embodiments of a point cloud showing confidence levels.
  • FIG. 20 illustrates an exemplary and non-limiting embodiment of differing confidence level metrics.
  • FIG. 21 illustrates an exemplary and non-limiting embodiment of a SLAM system.
  • FIG. 22 illustrates an exemplary and non-limiting embodiment of timing signals for the SLAM system.
  • FIG. 23 illustrates an exemplary and non-limiting embodiment of timing signals for the SLAM system.
  • FIG. 24 illustrates an exemplary and non-limiting embodiment of SLAM system signal synchronization.
  • FIG. 25 illustrates an exemplary and non-limiting embodiment of air-ground collaborative mapping.
  • FIG. 26 illustrates an exemplary and non-limiting embodiment of a sensor pack.
  • FIG. 27 illustrates an exemplary and non-limiting embodiment of a block diagram of the laser-visual-inertial odometry and mapping software system.
  • FIG. 28 illustrates an exemplary and non-limiting embodiment of a comparison of scans involved in odometry estimation and localization.
  • FIG. 29 illustrates an exemplary and non-limiting embodiment of a comparison of scan matching accuracy in localization.
  • FIG. 30 illustrates an exemplary and non-limiting embodiment of a horizontally orientated sensor test.
  • FIG. 31 illustrates an exemplary and non-limiting embodiment of a vertically orientated sensor test.
  • FIG. 32 illustrates an exemplary and non-limiting embodiment of an accuracy comparison between horizontally orientated and downward tilted sensor tests.
  • FIG. 33 illustrates an exemplary and non-limiting embodiment of an aircraft with a sensor pack.
  • FIG. 34 illustrates an exemplary and non-limiting embodiment of sensor trajectories.
  • FIG. 35 illustrates an exemplary and non-limiting embodiment of autonomous flight results.
  • FIG. 36 illustrates an exemplary and non-limiting embodiment of an autonomous flight result over a long-run.
  • FIGS. 37A and 37B illustrate an exemplary and non-limiting embodiment of a method creating a map that includes both geometry and sensor information and establishing position.
  • FIG. 38 is an exemplary and non-limiting embodiment of a heat map style representation of sensed information superimposed on floor plan geometry.
  • the present invention is directed to a mobile, computer- based mapping system that estimates changes in position over time (an odometer) and/or generates a three-dimensional map representation, such as a point cloud, of a three- dimensional space.
  • the mapping system may include, without limitation, a plurality of sensors including an inertial measurement unit (IMU), a camera, and/or a 3D laser scanner. It also may comprise a computer system, having at least one processor, in communication with the plurality of sensors, configured to process the outputs from the sensors in order to estimate the change in position of the system over time and/or generate the map
  • the mapping system may enable high- frequency, low-latency, on-line, real-time ego-motion estimation, along with dense, accurate 3D map registration.
  • Embodiments of the present disclosure may include a simultaneous location and mapping (SLAM) system.
  • the SLAM system may include a multidimensional (e.g., 3D) laser scanning and range measuring system that is GPS- independent and that provides real-time simultaneous location and mapping.
  • the SLAM system may generate and manage data for a very accurate point cloud that results from reflections of laser scanning from objects in an environment. Movements of any of the points in the point cloud are accurately tracked over time, so that the SLAM system can maintain precise understanding of its location and orientation as it travels through an environment, using the points in the point cloud as reference points for the location.
  • the resolution of the position and motion of the mobile mapping system may be sequentially refined in a series of coarse-to-fine updates.
  • discrete computational modules may be used to update the position and motion of the mobile mapping system from a coarse resolution having a rapid update rate, to a fine resolution having a slower update rate.
  • an IMU device may provide data to a first computational module to predict a motion or position of the mapping system at a high update rate.
  • a visual-inertial odometry system may provide data to a second
  • a laser scanner may provide data to a third computational, scan matching module to further refine the motion estimates and register maps at a still lower update rate.
  • data from a computational module configured to process fine positional and/or motion resolution data may be fed back to computational modules configured to process more coarse positional and/or motion resolution data.
  • the computational modules may incorporate fault tolerance to address issues of sensor degradation by automatically bypassing computational modules associated with sensors sourcing faulty, erroneous, incomplete, or non-existent data.
  • the mapping system may operate in the presence of highly dynamic motion as well as in dark, texture-less, and structure-less environments.
  • mapping system disclosed herein can operate in real-time and generate maps while in motion. This capability offers two practical advantages. First, users are not limited to scanners that are fixed on a tripod or other nonstationary mounting. Instead, the mapping system disclosed herein may be associated with a mobile device, thereby increasing the range of the environment that may be mapped in real-time. Second, the real-time feature can give users feedback for currently mapped areas while data are collected. The online generated maps can also assist robots or other devices for autonomous navigation and obstacle avoidance. In some non-limiting embodiments, such navigation capabilities may be incorporated into the mapping system itself. In alternative non-limiting embodiments, the map data may be provided to additional robots having navigation capabilities that may require an externally sourced map.
  • the mapping system can provide point cloud maps for other algorithms that take point clouds as input for further processing. Further, the mapping system can work both indoors and outdoors. Such embodiments do not require external lighting and can operate in darkness. Embodiments that have a camera can handle rapid motion, and can colorize laser point clouds with images from the camera, although external lighting may be required.
  • the SLAM system can build and maintain a point cloud in real time as a user is moving through an environment, such as when walking, biking, driving, flying, and combinations thereof. A map is constructed in real time as the mapper progresses through an environment. The SLAM system can track thousands of features as points.
  • the SLAM system operates in real time and without dependence on external location technologies, such as GPS.
  • a plurality (in most cases, a very large number) of features of an environment, such as objects, are used as points for triangulation, and the system performs and updates many location and orientation calculations in real time to maintain an accurate, current estimate of position and orientation as the SLAM system moves through an environment.
  • relative motion of features within the environment can be used to differentiate fixed features (such as walls, doors, windows, furniture, fixtures and the like) from moving features (such as people, vehicles, and other moving items), so that the fixed features can be used for position and orientation calculations.
  • Underwater SLAM systems may use blue-green lasers to reduce attenuation.
  • mapping system design follows an observation: drift in egomotion estimation has a lower frequency than a module's own frequency.
  • the three computational modules are therefore arranged in decreasing order of frequency. High-frequency modules are specialized to handle aggressive motion, while low-frequency modules cancel drift for the previous modules.
  • the sequential processing also favors computation: modules in the front take less computation and execute at high frequencies, giving sufficient time to modules in the back for thorough processing.
  • the mapping system is therefore able to achieve a high level of accuracy while running online in real-time.
  • the system may be configured to handle sensor degradation. If the camera is non-functional (for example, due to darkness, dramatic lighting changes, or texture-less environments) or if the laser is non-functional (for example due to structure-less
  • the corresponding module may be bypassed and the rest of the system may be staggered to function reliably.
  • the proposed pipeline automatically determines a degraded subspace in the problem state space, and solves the problem partially in the well-conditioned subspace. Consequently, the final solution is formed by combination of the "healthy" parts from each module.
  • the resulting combination of modules used to produce an output is neither simply a linear or non-linear combination of module outputs.
  • the output reflect a bypass of one or more entire modules in combination with a linear or non-linear combination of the remaining functioning modules. The system was tested through a large number of experiments and results show that it can produce high accuracy over several kilometers of navigation and robustness with respect to environmental degradation and aggressive motion.
  • the modularized mapping system is configured to process data from range, vision, and inertial sensors for motion estimation and mapping by using a multilayer optimization structure.
  • the modularized mapping system may achieve high accuracy, robustness, and low drift by incorporating features which may include:
  • mapping system for online ego-motion estimation with data from a 3D laser, a camera, and an IMU.
  • the estimated motion further registers laser points to build a map of the traversed environment.
  • ego-motion estimation and mapping must be conducted in real-time.
  • the map may be crucial for motion planning and obstacle avoidance, while the motion estimation is important for vehicle control and maneuver.
  • FIG 1 depicts a simplified block diagram of a mapping system 100 according to one embodiment of the present invention.
  • the illustrated system includes an IMU system 102 such as an Xsens® MTi-30 IMU, a camera system 104 such as an IDS® UI-1220SE monochrome camera, and a laser scanner 106 such as a Velodyne PUCKTM VLP-16 laser scanner.
  • IMU system 102 such as an Xsens® MTi-30 IMU
  • camera system 104 such as an IDS® UI-1220SE monochrome camera
  • a laser scanner 106 such as a Velodyne PUCKTM VLP-16 laser scanner.
  • the IMU 102 may provide inertial motion data derived from one or more of an x-y-z accelerometer, a roll-pitch-yaw gyroscope, and a magnetometer, and provide inertial data at a first frequency.
  • the first frequency may be about 200 Hz.
  • the camera system 104 may have a resolution of about 752x480 pixels, a 76° horizontal field of view (FOV), and a frame capture rate at a second frequency.
  • the frame capture rate may operate at a second frequency of about 50Hz.
  • the laser scanner 106 may have a 360° horizontal FOV, a 30° vertical FOV, and receive 0.3 million points/second at a third frequency representing the laser spinning rate.
  • the third frequency may be about 5Hz.
  • the laser scanner 106 may be connected to a motor 108 incorporating an encoder 109 to measure a motor rotation angle.
  • the laser motor encoder 109 may operate with a resolution of about 0.25° ⁇
  • the IMU 102, camera 104, laser scanner 106, and laser scanner motor encoder 109 may be in data communication with a computer system 110, which may be any computing device, having one or more processors 134 and associated memory 120, 160, having sufficient processing power and memory for performing the desired odometry and/or mapping.
  • a computer system 110 may be any computing device, having one or more processors 134 and associated memory 120, 160, having sufficient processing power and memory for performing the desired odometry and/or mapping.
  • a laptop computer with 2.6GHz i7quad-core processor (2 threads on each core and 8 threads overall) and an integrated GPU memory could be used.
  • the computer system may have one or more types of primary or dynamic memory 120 such as RAM, and one or more types of secondary or storage memory 160 such as a hard disk or a flash ROM.
  • the mapping system 100 incorporates a computational model comprising individual computational modules that sequentially recover motion in a coarse-to-fine manner (see also FIG. 2).
  • a visual-inertial tightly coupled method (visual-inertial odometry module 126) estimates motion and registers laser points locally.
  • a scan matching method scan matching refinement module 132) further refines the estimated motion.
  • the scan matching refinement module 132 also registers point cloud data 165 to build a map (voxel map 134).
  • the map also may be used by the mapping system as part of an optional navigation system 136. It may be recognized that the navigation system 136 may be included as a computational module within the onboard computer system, the primary memory, or may comprise a separate system entirely.
  • each computational module may process data from one of each of the sensor systems.
  • the IMU prediction module 122 produces a coarse map from data derived from the IMU system 102
  • the visual-inertial odometry module 126 processes the more refined data from the camera system 104
  • the scan matching refinement module 132 processes the most fine-grained resolution data from the laser scanner 106 and the motor encoder 109.
  • each of the finer-grained resolution modules further process data presented from a coarser-grained module.
  • the visual-inertial odometry module 126 refines mapping data received from and calculated by the IMU prediction module 122.
  • the scan matching refinement module 132 further processes data presented by the visual inertial odometry module 126.
  • each of the sensor systems acquires data at a different rate.
  • the IMU 102 may update its data acquisition at a rate of about 200 Hz
  • the camera 104 may update its data acquisition at a rate of about 50 Hz
  • the laser scanner 106 may update its data acquisition at a rate of about 5 Hz.
  • These rates are non-limiting and may, for example, reflect the data acquisition rates of the respective sensors. It may be recognized that coarse-grained data may be acquired at a faster rate than more fine-grained data, and the coarse-grained data may also be processed at a faster rate than the fine-grained data.
  • specific frequency values for the data acquisition and processing by the various computation modules are disclosed above, neither the absolute frequencies nor their relative frequencies are limiting.
  • the mapping and/or navigational data may also be considered to comprise coarse level data and fine level data.
  • coarse positional data may be stored in a voxel map 134 that may be accessible by any of the computational modules 122, 126, 132.
  • File detailed mapping data, as point cloud data 165 that may be produced by the scan matching refinement module 132, may be stored via the processor 150 in a secondary memory 160, such as a hard drive, flash drive, or other more permanent memory.
  • both the visual-inertial odometry module 126 and the scan matching refinement module 132 can feed back their more refined mapping data to the IMU prediction module 122 via respective feedback paths 128 and 138 as a basis for updating the IMU position prediction.
  • coarse positional and mapping data may be sequentially refined in resolution, and the refined resolution data serve as feed-back references for the more coarse resolution computations.
  • FIG. 2 depicts a block diagram of the three computational modules along with their respective data paths.
  • the IMU prediction module 122 may receive IMU positional data 223 from the IMU (102, FIG. 1).
  • the visual-inertial odometry module 126 may receive the model data from the IMU prediction module 122 as well as visual data from one or more individually tracked visual features 227a, 227b from the camera (104, FIG. 1).
  • the laser scanner (106, FIG. 1) may produce data related to laser determined landmarks 233a, 233b, which may be supplied to the scan matching refinement module 132 in addition to the positional data supplied by the visual-inertial odometry module 126.
  • the positional estimation model from the visual-inertial odometry module 126 may be fed back 128 to refine the positional model calculated by the IMU prediction module 122.
  • the refined map data from the scan matching refinement module 132 may be fed back 138 to provide additional correction to the positional model calculated by the IMU prediction module 122.
  • the modularized mapping system may sequentially recover and refine motion related data in a coarse-to-fine manner.
  • each module may be determined by the data acquisition and processing rate of each of the devices sourcing the data to the modules.
  • a visual-inertial tightly coupled method estimates motion and registers laser points locally.
  • a scan matching method further refines the estimated motion.
  • the scan matching refinement module may also register point clouds to build a map.
  • the mapping system is time optimized to process each refinement phase as data become available.
  • FIG. 3 illustrates a standard Kalman filter model based on data derived from the same sensor types as depicted in Figure 1.
  • the Kalman filter model updates positional and/or mapping data upon receipt of any data from any of the sensors regardless of the resolution capabilities of the data.
  • the positional information may be updated using the visual-inertial odometry data at any time such data become available regardless of the state of the positional information estimate based on the IMU data.
  • the Kalman filter model therefore does not take advantage of the relative resolution of each type of measurement.
  • FIG. 3 depicts a block diagram of a standard Kalman filter based method for optimizing positional data.
  • the Kalman filter updates a positional model 322a - 322n sequentially as data are presented.
  • the Kalman filter may predict 324a the subsequent positional model 322b. which may be refined based on the receive IMU mechanization data 323.
  • the positional prediction model may be updated 322b in response to the IMU mechanization data 323. in a prediction step 324a followed by update steps seeded with individual visual features or laser landmarks.
  • FIG. 4 depicts positional optimization based on a factor-graph method.
  • a pose of a mobile mapping system at a first time 410 may be updated upon receipt of data to a pose at a second time 420.
  • a factor-graph optimization model combines constraints from all sensors during each refinement calculation.
  • IMU data 323, feature data 327a, 327b, and similar from the camera, and laser landmark data 333a, 333b, and similar are all used for each update step. It may be appreciated that such a method increases the computational complexity for each positional refinement step due to the large amount of data required.
  • the sensors may provide data at independent rates that may differ by orders of magnitude, the entire refinement step is time bound to the data acquisition time for the slowest sensor.
  • FIGS. 1 and 2 sequentially recovers motion in a coarse-to-fine manner. In this manner, the degree of motion refinement is determined by the availability of each type of data.
  • a sensor system of a mobile mapping system may include a laser 106, a camera 104, and an IMU 102.
  • the camera may be modeled as a pinhole camera model for which the intrinsic parameters are known. The extrinsic parameters among all of the three sensors may be calibrated.
  • the relative pose between the camera and the laser and the relative pose between the laser and the IMU may be determined according to methods known in the art.
  • a single coordinate system may be used for the camera and the laser.
  • the camera coordinate system may be used, and all laser points may be projected into the camera coordinate system in pre-processing.
  • the IMU coordinate system may be parallel to the camera coordinate system and thus the IMU measurements may be rotationally corrected upon acquisition.
  • the coordinate systems may be defined as follows:
  • the camera coordinate system ⁇ C ⁇ may originate at the camera optical center, in which the x-axis points to the left, the _ -axis points upward, and the z-axis points forward coinciding with the camera principal axis;
  • the IMU coordinate system ⁇ 1 ⁇ may originate at the IMU measurement center, in which the x-, y-, and z- axes are parallel to ⁇ C ⁇ and pointing in the same directions;
  • ⁇ W ⁇ may be the coordinate system coinciding with ⁇ C ⁇ at the starting pose.
  • the landmark positions are not necessarily optimized. As a result, there remain six unknowns in the state space thus keeping computation intensity low.
  • the disclosed method involves laser range measurements to provide precise depth information to features, warranting motion estimation accuracy while further optimizing the features' depth in a bundle. One need only optimize some portion of these measurements as further optimization may result in diminishing returns in certain circumstances.
  • calibration of the described system may be based, at least in part, on the mechanical geometry of the system.
  • the LIDAR may be calibrated relative to the motor shaft using mechanical measurements from the CAD model of the system for geometric relationships between the lidar and the motor shaft.
  • Such calibration as is obtained with reference to the CAD model has been shown to provide high accuracy and drift without the need to perform additional calibration.
  • a state estimation problem can be formulated as a maximum a posterior (MAP) estimation problem.
  • ⁇ ⁇ ; ⁇ , i E ⁇ 1; 2; ... , m]
  • U ⁇ Wj ⁇ , ⁇ E (1; 2; ... , m ⁇
  • Z ⁇ z k ⁇ , k E (1; 2; ... , n ⁇ , as the set of landmark measurements.
  • Z may be composed of both visual features and laser landmarks.
  • the joint probability of the system is defined as follows,
  • P(xo) is a prior of the first system state
  • ) represents the motion model
  • P(z k ⁇ x ik ) represents the landmark measurement model.
  • the MAP estimation is to maximize Eq. 1. Under the assumption of zero-mean Gaussian noise, the problem is equivalent to a least-square problem,
  • r x t and r are residual errors associated with the motion model and the landmark measurement model, respectively.
  • the standard way of solving Eq. 2 is to combine all sensor data, for example visual features, laser landmarks, and IMU measurements, into a large factor-graph optimization problem.
  • the proposed data processing pipeline instead, formulates multiple small optimization problems and solves the problems in a coarse-to-fine manner.
  • the optimization problem may be restated as:
  • ⁇ C ⁇ the fundamental sensor coordinate system
  • the IMU may also be characterized with respect to ⁇ C ⁇ .
  • ⁇ ( ⁇ ) and a(t) may be two 3 x 1 vectors indicating the angular rates and accelerations, respectively, of ⁇ C ⁇ at time t.
  • the corresponding biases may be denoted as b m (t) and b a (t) and n m (t) and n a (t) be the corresponding noises.
  • the vector, bias, and noise terms are defined in ⁇ C ⁇ .
  • g may be denoted as the constant gravity vector in ⁇ W ⁇ .
  • ⁇ R(t) is the rotation matrix from ⁇ W ⁇ to ⁇ C ⁇
  • t is the translation vector between
  • the term ⁇ t ⁇ >(t) ⁇ 2 represents the centrifugal force due to the fact that the rotation center (origin of ⁇ C ⁇ ) is different from the origin of ⁇ / ⁇ .
  • Some examples of visual-inertial navigation methods model the motion in ⁇ / ⁇ to eliminate this centrifugal force term.
  • converting features without depth from ⁇ C ⁇ to ⁇ / ⁇ is not straight forward (see below).
  • the system disclosed herein models all of the motion in ⁇ C ⁇ instead. Practically, the camera and the IMU are mounted close to each other to maximally reduce effect of the term.
  • the IMU biases may be slowly changing variables. Consequently, the most recently updated biases are used for motion integration. First, Eq. 3 is integrated over time. Then, the resulting orientation is used with Eq. 4 for integration over time twice to obtain translation from the acceleration data.
  • the IMU bias correction can be made by feedback from either the camera or the laser (see 128, 138, respectively, in FIGS. 1 and 2). Each feedback term contains the estimated incremental motion over a short amount of time.
  • the biases may be modeled to be constant during the incremental motion.
  • b m (t) may be calculated by comparing the estimated orientation with IMU integration.
  • the updated b m (t) is used in one more round of integration to re-compute the translation, which is compared with the estimated translation to calculate b a (t).
  • a sliding window is employed keeping a known number of biases.
  • the number of biases used in the sliding window may include 200 to 1000 biases with a recommended number of 400 biases based on a 200Hz IMU rate.
  • a non- limiting example of the number of biases in the sliding window with an IMU rate of 100Hz is 100 to 500 with a typical value of 200 biases.
  • the averaged biases from the sliding window are used.
  • the length of the sliding window functions as a parameter for determining an update rate of the biases.
  • the disclosed implementation is used in order to keep the IMU processing module as a separate and distinct module.
  • the sliding window method may also allow for dynamic reconfiguration of the system.
  • the IMU can be coupled with either the camera, the laser, or both camera and laser as required. For example, if the camera is non- functional, the IMU biases may be corrected only by the laser instead.
  • a sliding window may be employed keeping a certain number of biases.
  • the averaged biases from the sliding window may be used.
  • the length of the sliding window functions as a parameter determining an update rate of the biases.
  • the biases may be modeled as random walks and the biases updated through a process of optimization.
  • this non-standard implementation is preferred to keep IMU processing in a separate module.
  • the implementation favors dynamic reconfiguration of the system, i.e. the IMU may be coupled with either the camera or the laser. If the camera is non-functional, the IMU biases may be corrected by the laser instead.
  • inter-module communication in the sequential modularized system is utilized to fix the IMU biases. This communication enables IMU biases to be corrected.
  • IMU bias correction may be accomplished by utilizing feedback from either the camera or the laser.
  • Each of the camera and the laser contains the estimated incremental motion over a short amount of time.
  • the methods and systems described herein model the biases to be constant during the incremental motion.
  • the methods and systems described herein can calculate £> ⁇ (t).
  • the updated £> ⁇ ( ⁇ ) is used in one more round of integration to recompute the translation, which is compared with the estimated translation to calculate b a (t).
  • IMU output comprises an angular rate having relatively constant errors over time.
  • the resulting IMU bias is related to the fact that the IMU will always have some difference from ground truth. This bias can change over time. It is relatively constant and not high frequency.
  • the sliding window described above is a specified period of time during which the IMU data is evaluated.
  • FIG. 5 there is provided a system diagram of the visual-inertial odometry subsystem.
  • the method couples vision with an IMU. Both vision and the IMU provide constraints to an optimization problem that estimates incremental motion.
  • the method associates depth information to visual features. If a feature is located in an area where laser range measurements are available, depth may be obtained from laser points. Otherwise, depth may be calculated from triangulation using the previously estimated motion sequence.
  • the method may also use features without any depth by formulating constraints in a different way. This is true for those features which may not necessarily have laser range coverage or cannot be triangulated due to the fact that they are not necessarily tracked long enough or located in the direction of camera motion.
  • a block system diagram of the visual-inertial odometry subsystem is depicted in Fig. 5.
  • An optimization module 510 uses pose constraints 512 from the IMU prediction module 520 along with camera constraints 515 based on optical feature data having or lacking depth information for motion estimation 550.
  • a depthmap registration module 545 may include depthmap registration and depth association of the tracked camera features 530 with depth information obtained from the laser points 540.
  • the depthmap registration module 545 may also incorporate motion estimation 550 obtained from a previous calculation.
  • the method tightly couples vision with an IMU.
  • Each provides constraints 512, 515, respectively, to an optimization module 510 that estimates incremental motion 550.
  • the method associates depth information to visual features as part of the depthmap registration module 545.
  • depth is obtained from laser points. Otherwise, depth is calculated from triangulation using the previously estimated motion sequence.
  • the method can also use features without any depth by formulating constraints in a different way. This is true for those features which do not have laser range coverage or cannot be triangulated because they are not tracked long enough or located in the direction of camera motion.
  • the visual-inertial odometry is a key-frame based method.
  • a new key-frame is determined 535 if more than a certain number of features lose tracking or the image overlap is below a certain ratio.
  • right superscript /, / £ Z + may indicate the last key-frame
  • c, c £ Z + and o k may indicate the current frame.
  • the method combines features with and without depth.
  • Xi, Xi, xi, and xi are different from ⁇ and x in Eq.1 which represent the system state.
  • Features at key-frames may be associated with depth for two reasons: 1) depth association takes some amount of processing, and computing depth association only at key-frames may reduce computation intensity; and 2) the depthmap may not be available at frame c and thus laser points may not be registered since registration depends on an established depthmap.
  • R; and tf be the 3 x 3 rotation matrix and 3 x 1 translation vector between frames / and c, where Rf £ SO(3) and tf £ 3 , Rf and Tf form an SE(3) transformation.
  • the motion function between frames / and c may be written as
  • R(h) and t(h), h E ⁇ 1, 2, 3 ⁇ are the h-th rows of R[ and tf .
  • di the unknown depth at key-frame /.
  • the motion estimation process 510 is required to solve an optimization problem combining three sets of constraints: 1) from features with known depth as in Eqs. 6-7; 2) from features with unknown depth as in Eq. 8; and 3) from the IMU prediction 520.
  • ⁇ may be defined as a 4 x 4 transformation matrix between frames a and b,
  • the solved motion transform between frames / and c - I may be used to formulate the IMU pose constraints.
  • a predicted transform between the last two frames c - 1 and c, denoted as T ⁇ may be obtained from IMU mechanization.
  • the predicted transform at frame c is calculated as,
  • ⁇ and if be the 6-DOF motion corresponding to Tf.
  • the IMU predicted translation, if, is dependent on the orientation.
  • the orientation may determine a projection of the gravity vector through rotation matrix ⁇ R(t) in Eq. 4, and hence the accelerations that are integrated, if may be formulated as a function of Of, and may be rewriten as if (0 ).
  • the 200Hz pose provided by the IMU prediction module 122 (FIGS. 1 and 2) as well as the 50Hz pose provided by the visual - inertial odometry module 126 (FIGS. 1 and 2) are both pose functions.
  • the pose constraints fulfill the motion model and the camera constraints fulfill the landmark measurement model in Eq. 2.
  • the optimization problem may be solved by using the Newton gradient-descent method adapted to a robust fitting framework for outlier feature removal.
  • the state space contains ⁇ and tf.
  • a full-scale MAP estimation is not performed, but is used only to solve a marginalized problem.
  • the landmark positions are not optimized, and thus only six unknowns in the state space are used, thereby keeping computation intensity low.
  • the method thus involves laser range measurements to provide precise depth information to features, warranting motion estimation accuracy. As a result, further optimization of the features' depth through a bundle adjustment may not be necessary.
  • the depthmap registration module 545 registers laser points on a depthmap using previously estimated motion.
  • Laser points 540 within the camera field of view are kept for a certain amount of time.
  • the depthmap is down-sampled to keep a constant density and stored in a 2D KD-tree for fast indexing.
  • KD-tree all laser points are projected onto a unit sphere around the camera center. A point is represented by its two angular coordinates.
  • features may be projected onto the sphere.
  • the three closest laser points are found on the sphere for each feature. Then, their validity may be by calculating distances among the three points in Cartesian space.
  • the depth is interpolated from the three points assuming a local planar patch in Cartesian space.
  • Those features without laser range coverage if they are tracked over a certain distance and not located in the direction of camera motion, may be triangulated using the image sequences where the features are tracked. In such a procedure, the depth may be updated at each frame based on a Bayesian probabilistic mode.
  • FIG. 6 depicts a block diagram of the scan matching subsystem.
  • the subsystem receives laser points 540 in a local point cloud and registers them 620 using provided odometry estimation 550. Then, geometric features are detected 640 from the point cloud and matched to the map. The scan matching minimizes the feature-to-map distances, similar to many methods known in the art.
  • the odometry estimation 550 also provides pose constraints 612 in the optimization 610.
  • the optimization comprises processing pose constraints with feature correspondences 615 that are found and further processed with laser constraints 617 to produce a device pose 650.
  • This pose 650 is processed through a map registration process 655 that facilitates finding the feature correspondences 615.
  • the implementation uses voxel representation of the map. Further, it can dynamically configure to run on one to multiple CPU threads in parallel.
  • the method When receiving laser scans, the method first registers points from a scan 620 into a common coordinate system. m, m E Z + may be used to indicate the scan number. It is understood that the camera coordinate system may be used for both the camera and the laser. Scan m may be associated with the camera coordinate system at the beginning of the scan, denoted as ⁇ C m ⁇ . To locally register 620 the laser points 540, the odometry estimation 550 from the visual-inertial odometry may be taken as key-points, and the IMU measurements may be used to interpolate in between the key-points.
  • P m be the locally registered point cloud from scan m.
  • Two sets of geometric features from P m may be extracted: one on sharp edges, namely edge points and denoted as e m , and the other on local planar surfaces, namely planar points and denoted as H m .
  • This is through computation of curvature in the local scans. Points whose neighbor points are already selected are avoided such as points on boundaries of occluded regions and points whose local surfaces are close to be parallel to laser beams. These points are likely to contain large noises or change positions over time as the sensor moves.
  • the scan matching is formulated into an optimization problem 610 minimizing the overall distances described by Eq. 12.
  • the optimization also involves pose constraints 612 from prior motion.
  • T m _ t be the 4 x 4 transformation matrix regarding the pose of ⁇ Cm— 1 ⁇ in ⁇ W ⁇
  • Tm-i is generated by processing the last scan.
  • T ⁇ _ t be, the pose transform from ⁇ Cm-i ⁇ to ⁇ Cm ⁇ , as provided by the odometry estimation. Similar to Eq. 10, the predicted pose transform of ⁇ C m ⁇ in ⁇ W ⁇ is,
  • Eq. 14 refers to the case that the prior motion is from the visual-inertial odometry, assuming the camera is functional. Otherwise, the constraints are from the IMU prediction. ⁇ ' m and t' m (# m ) may be used to denote the same terms by IMU mechanization. t' m (# m ) is a function of Q m because integration of accelerations is dependent on the orientation (same with tf (6f) in Eq. 11). The IMU pose constraints are,
  • Eqs. 14 and 15 are linearly combined into one set of constraints. The linear combination is determined by working mode of the visual-inertial odometry. The optimization problem refines Q m and t m , which is solved by the Newton gradient-descent method adapted to a robust fitting framework.
  • M m _ denotes the set of voxels 702, 704 on the first level map 700 after processing the last scan.
  • Voxels 704 surrounding the sensor 706 form a subset M m _ ! , denoted as S m _i.
  • S m _i Given a 6-DOF sensor pose, Q m and t m , there is a corresponding S m _ t which moves with the sensor on the map.
  • voxels on the opposite side 725 of the boundary are moved over to extend the map boundary 730. Points in moved voxels are cleared resulting in truncation of the map.
  • each voxel j,j £ £ S m _ t of the second level map 750 is formed by a set of voxels that are a magnitude smaller, denoted as S m 1 _ 1 than those of the first level map 700.
  • S m 1 _ 1 a magnitude smaller, denoted as S m 1 _ 1 than those of the first level map 700.
  • points in £ m and H m are projected onto the map using the best guess of motion, and fill them into [S ⁇ .- , j ⁇ S-m-i- Voxels 708 occupied by points from £ m and H m are extracted to form Q m - ⁇ an d stored in 3D KD-trees for scan matching.
  • Voxels 710 are those not occupied by points from £ m or H m .
  • each voxel of the first level map 700 corresponds to a volume of space that is larger than a sub-voxel of the second level map 750.
  • each voxel of the first level map 700 comprises a plurality of sub- voxels in the second level map 750 and can be mapped onto the plurality of sub- voxels in the second level map 750.
  • first level map 700 and second level map 750 two levels are used to store map information.
  • Voxels corresponding to m _ ! are used to maintain the first level map 700 and voxels corresponding to [S m J _ ⁇ , j £ S m -i in the second level map 750 are used to retrieve the map around the sensor for scan matching.
  • the map is truncated only when the sensor approaches the map boundary. Thus, if the sensor navigates inside the map, no truncation is needed.
  • KD-trees are used for each individual voxel in S m _ t - one for edge points and the other for planar points.
  • a data structure may accelerate point searching. In this manner, searching among multiple KD-trees is avoided as opposed to using two KD- trees for each individual voxel in [S ⁇ .- , j ⁇ S-m-i- The later requires more resources for KD-tree building and maintenance.
  • Table 1 compares CPU processing time using different voxel and KD-tree configurations. The time is averaged from multiple datasets collected from different types of environments covering confined and open, structured and vegetated areas. We see that using only one level of voxels, M m _ t , results in about twice of processing time for KD-tree building and querying. This is because the second level of voxels, [S m J _ ⁇ , j S ⁇ -L, help retrieve the map precisely. Without these voxel, more points are contained in Q m - ⁇ an d built into the KD-trees. Also, by using KD-trees for each voxel, processing time is reduced slightly in comparison to using KD-trees for all voxels in M m _ t .
  • FIG. 8A illustrates the case where two matcher programs 812, 815 run in parallel.
  • a manager program 810 arranges it to match with the latest map available.
  • matching is slow and may not complete before arrival of the next scan.
  • the two matchers 812 and 815 are called alternatively.
  • the final motion estimation is integration of outputs from the three modules depicted in FIG. 2.
  • the 5Hz scan matching output produces the most accurate map, while the 50Hz visual-inertial odometry output and the 200Hz IMU prediction are integrated for high- frequency motion estimates.
  • the robustness of the system is determined by its ability to handle sensor degradation.
  • the IMU is always assumed to be reliable functioning as the backbone in the system.
  • the camera is sensitive to dramatic lighting changes and may also fail in a dark/texture-less environment or when significant motion blur is present (thereby causing a loss of visual features tracking).
  • the laser cannot handle structure-less environments, for example a scene that is dominant by a single plane.
  • laser data degradation can be caused by sparsity of the data due to aggressive motion.
  • Such aggressive motion comprises highly dynamic motion.
  • “highly dynamic motion” refers to substantially abrupt rotational or linear displacement of the system or continuous rotational or translational motion having a substantially large magnitude.
  • the disclosed self-motion determining system may operate in the presence of highly dynamic motion as well as in dark, texture-less, and structure-less environments.
  • the system may operate while experiencing angular rates of rotation as high as 360 deg per second.
  • the system may operate at linear velocities up to and including at HOkph.
  • these motions can be coupled angular and linear motions.
  • Both the visual-inertial odometry and the scan matching modules formulate and solve optimization problems according to EQ. 2.
  • a failure happens, it corresponds to a degraded optimization problem, i.e. constraints in some directions of the problem are ill- conditioned and noise dominates in determining the solution.
  • eigenvalues denoted as ⁇ , ⁇ 2, A6, and eigenvectors, denoted as vi, v 2 , ve, associated with the problem may be computed.
  • Six eigenvalues/eigenvectors are present because the state space of the sensor contains 6-DOF (6 degrees of freedom). Without losing generality, vi, V2, .. ⁇ , V6 may be sorted in decreasing order.
  • Each eigenvalue describes how well the solution is conditioned in the direction of its corresponding eigenvector.
  • well-conditioned directions may be separated from degraded directions in the state space.
  • Let h, h 0; 1, 6, be the number of well-conditioned directions.
  • Two matrices may be defined as:
  • the nonlinear iteration may start with an initial guess.
  • the IMU prediction provides the initial guess for the visual-inertial odometry, whose output is taken as the initial guess for the scan matching.
  • x be a solution
  • Ax be an update of x in a nonlinear iteration, in which Ax is calculated by solving the linearized system equations.
  • x may be updated only in well-conditioned directions, keeping the initial guess in degraded directions instead,
  • the system solves for motion in a coarse-to-fine order, starting with the IMU prediction, the additional two modules further solving/refining the motion as much as possible. If the problem is well-conditioned, the refinement may include all 6-DOF.
  • the refinement may include 0 to 5-DOF. If the problem is completely degraded, V becomes a zero matrix and the previous module's output is kept.
  • Vy and Vy denote the matrices containing eigenvectors from the visual- inertial odometry module, Vy represents well-conditioned directions in the subsystem, and Vy— Vy represents degraded directions.
  • the combined constraints are,
  • V v is a zero matrix and Eq. 18 is composed of pose constraints from the IMU prediction according to Eq. 15.
  • the IMU prediction 122 bypasses the visual-inertial odometry module 126 fully or partially 924 - denoted by the dotted line— depending on the number of well-conditioned directions in the visual-inertial odometry problem.
  • the scan matching module 132 may then locally register laser points for the scan matching.
  • the bypassing IMU prediction is subject to drift.
  • the laser feedback 138 compensates for the camera feedback 128 correcting velocity drift and biases of the IMU, only in directions where the camera feedback 128 is unavailable. Thus, the camera feedback has a higher priority, due to the higher frequency making it more suitable when the camera data are not degraded. When sufficient visual features are found, the laser feedback is not used.
  • the visual-inertial odometry module 126 output fully or partially bypasses the scan matching module to register laser points on the map 930 as noted by the dotted line. If well-conditioned directions exist in the scan matching problem, the laser feedback contains refined motion estimates in those directions. Otherwise, the laser feedback becomes empty 138.
  • FIG. 10 depicts such an example.
  • a vertical bar with six rows represents a 6- DOF pose where each row is a DOF (degree of freedom), corresponding to an eigenvector in EQ. 16.
  • the visual-inertial odometry and the scan matching each updates a 3- DOF of motion, leaving the motion unchanged in the other 3 -DOF.
  • the IMU prediction 1022a-f may include initial IMU predicted values 1002.
  • the scan matching updates 1006 some 3-DOF (1032b, 1032d, 1032f) resulting in a further refined prediction 1032a-1032f.
  • the camera feedback 128 contains camera updates 1028a- 1028f and the laser feedback 138 contains laser updates 1038a-1038f, respectively.
  • cells having no shading (1028a, 1028b, 1028d, 1038a, 1038c, 1038e) do not contain any updating information from the respective modules.
  • the 1080a-1080f to the IMU prediction modules is a combination of the updates 1028a-1028f from the camera feedback 128 and the updates 1038a- 1038f from the laser feedback 138.
  • the camera updates (for example 1028f) may have priority over the laser updates (for example 1038f).
  • the visual-inertial odometry module and the scan matching module may execute at different frequencies and each may have its own degraded directions.
  • IMU messages may be used to interpolate between the poses from the scan matching output.
  • an incremental motion that is time aligned with the visual-inertial odometry output may be created.
  • Let an d t c c _ t be the 6-DOF motion estimated by the visual- inertial odometry between frames c— 1 and c, where ⁇ 0 °_ ⁇ £ so(3) and t c c _ 1 E M. 3 .
  • Let 0'c-i and t'c-i be the corresponding terms estimated by the scan matching after time interpolation.
  • V v and V v may be the matrices defined in Eq. 16 containing eigenvectors from the visual-inertial odometry module, in which V v represents well-conditioned directions, and V v — Vy represents degraded directions.
  • V s and and V s be the same matrices from the scan matching module. The following equation calculates the combined feedback, f c ,
  • f c only contains solved motion in a subspace of the state space.
  • ⁇ £- ⁇ ⁇ & ⁇ ( ) and t c c _ 1 (b a) (t), b a (t)) may be used to denote the IMU predicted motion formulated as functions of ⁇ > ⁇ ( and b a t) through integration of Eqs. 3 and 4.
  • the orientation 0£_ ⁇ ( ⁇ » ⁇ ( ⁇ ) is only relevant to b Cl) (t), but the translation t c c _ 1 ( ⁇ b Cl) (t), fc a (t)) is dependent on both b ⁇ t) and b a t) .
  • the biases can be calculated by solving the following equation,
  • f c spans the state space, and V v — Vy and Ys— V s in Eq. 22 are zero matrices.
  • b CJ t) and b a (t) are calculated from f c .
  • the IMU predicted motion, andt ⁇ is used in directions where the motion is unsolvable (e.g. white row 1080a of the combined feedback in FIG. 10). The result is that the previously calculated biases are kept in these directions.
  • a Velodyne LIDARTM HDL-32E laser scanner is attached to a UI- 1220SE monochrome camera and an Xsens ® MTi-30 IMU.
  • the laser scanner has 360° horizontal FOV, 40° vertical FOV, and receives 0.7 million points/second at 5Hz spinning rate.
  • the camera is configured at the resolution of 752 x 480 pixels, 76° horizontal FOV, and 50Hz frame rate.
  • the IMU frequency is set at 200Hz.
  • a Velodyne LIDARTM VLP-16 laser scanner is attached to the same camera and IMU. This laser scanner has 360° horizontal FOV, 30° vertical FOV, and receives 0.3 million points/second at 5Hz spinning rate.
  • Each sensor suite is attached to a vehicle for data collection, which are driven on streets and in off-road terrains, respectively.
  • the software runs on a laptop computer with a 2.6GHz i7 quad-core processor (2 threads on each core and 8 threads overall) and an integrated GPU, in a Linux ® system running Robot Operating System (ROS).
  • ROS Robot Operating System
  • Two versions of the software were implemented with visual feature tracking running on GPU and CPU, respectively.
  • the processing time is shown in Table 2.
  • the time used by the visual-inertial odometry (126 in FIG. 2) does not vary much with respect to the environment or sensor configuration.
  • For the GPU version it consumes about 25% of a CPU thread executing at 50Hz.
  • For the CPU version it takes about 75% of a thread.
  • the sensor first suite results in slightly more processing time than the second sensor suite. This is because the scanner receives more points and the program needs more time to maintain the depthmap and associate depth to the visual features.
  • the scan matching consumes more processing time which also varies with respect to the environment and sensor configuration.
  • the scan matching takes about 75% of a thread executing at 5Hz if operated in structured environments. In vegetated environments, however, more points are registered on the map and the program typically consumes about 135% of a thread.
  • the scanner receives fewer points.
  • the scan matching module 132 uses about 50-95% of a thread depending on the environment.
  • the time used by the IMU prediction (132 in FIG. 2) is negligible compared to the other two modules.
  • Tests were conducted to evaluate accuracy of the proposed system. In these tests, the first sensor suite was used. The sensors were mounted on an off -road vehicle driving around a university campus. After 2.7km of driving within 16 minutes, a campus map was built. The average speed over the test was 2.8m/s.
  • FIG. 11 depicts estimated trajectories in an accuracy test.
  • a first trajectory plot 1102 of the trajectory of a mobile sensor generated by the visual-inertial odometry system uses the IMU module 122 and the visual-inertial odometry module 126 (see FIG. 2). The configuration used in the first trajectory plot 1102 is similar to that depicted in FIG. 9B.
  • a second trajectory plot 1104 is based on directly forwarding the IMU prediction from the IMU module 122 to the scan matching module, 132 (see FIG. 2) bypassing the visual-inertial odometry. This configuration is similar to that depicted in FIG. 9A.
  • a third trajectory plot 1108 of the complete pipeline is based on the combination of the IMU module 122, the visual inertial odometry module 126, and the scan matching module 132 (see FIG. 2) has the least amount of drift.
  • the position errors of the first two configurations, trajectory plot 1102 and 1104, are about four and two times larger, respectively.
  • the first trajectory plot 1102 and the second trajectory plot 1104 can be viewed as the expected system performance when encountering individual sensor degradation. If scan matching is degraded (see FIG. 9B), the system reduces to a mode indicated by the first trajectory plot 1102. If vision is degraded, (see FIG. 9A), the system reduces to a mode indicated by the second trajectory plot 1104. If none of the sensors is degraded, (see FIG 2) the system incorporates all of the optimization functions resulting in the trajectory plot 1108. In another example, the system may take the IMU prediction as the initial guess and but run at the laser frequency (5Hz). The system produces a fourth trajectory plot 1106.
  • Another accuracy test of the system included running mobile sensor at the original lx speed and an accelerated 2x speed. When running at 2x speed, every other data frame for all three sensors is omitted, resulting in much more aggressive motion through the test. The results are listed in Table 3. At each speed, the three configurations were evaluated. At 2x speed, the accuracy of the visual-inertial odometry and the IMU + scan matching
  • FIG. 12 there is illustrated an exemplary and non-limiting embodiment of bidirectional information flow.
  • three modules comprising an IMU prediction module, a visual-inertial odometry module and a scan-matching refinement module solve the problem step by step from coarse to fine.
  • Data processing flow is from left to right passing the three modules respectively, while feedback flow is from right to left to correct the biases of the IMU.
  • FIG. 13a With reference to Figs. 13a and 13b, there is illustrated an exemplary and non- limiting embodiment of a dynamically reconfigurable system.
  • the IMU prediction (partially) bypasses the visual-inertial odometry module to register laser points locally.
  • the visual-inertial odometry output (partially) bypasses the scan matching refinement module to register laser points on the map.
  • a vertical bar represents a 6-DOF pose and each row is a DOF.
  • the visual-inertial odometry updates in 3-DOF where the rows become designated "camera” then the scan matching updates in another 3-DOF where the rows turn designated "laser”.
  • the camera and the laser feedback is combined as the vertical bar on the left.
  • the camera feedback has a higher priority - "laser” rows from the laser feedback are only filled in if "camera” rows from the camera feedback are not present.
  • FIG. 15a and 15b there is illustrated an exemplary and non- limiting embodiment of two-layer voxel representation of a map.
  • voxels on the map M all voxels in Fig. 15a
  • voxels surrounding the sensor S m _ 1 dot filled voxels.
  • S m _ t is a subset of M m _ t . If the sensor approaches the boundary of the map, voxels on the opposite side of the boundary (bottom row) are moved over to extend the map boundary. Points in moved voxels are cleared and the map is truncated.
  • each voxel j, j E S m _ t (a dot filled voxel in Fig. 15a) is formed by a set of voxels S m 1 _ 1 that are a magnitude smaller (all voxels in (Fig. 15b) £ S ⁇ -i)-
  • the laser scan may be projected onto the map using the best guess of motion.
  • Voxels in (S ⁇ - i ), j ⁇ Sm-i occupied by points from the scan are labeled in cross-hatch.
  • map points in cross-hatched voxels are extracted and stored in 3D KD-trees for scan matching.
  • FIG. 16 there is illustrated an exemplary and non-limiting embodiment of multi-thread processing of scan matching.
  • a manager program calls multiple matcher programs running on separate CPU threads and matches scans to the latest map available.
  • Fig. 16a shows a two-thread case. Scans P m , P m -i, are matched with map Q m , Q m -i, on each matcher, giving twice amount of time for processing.
  • Fig. 16b shows a one-thread case, where P m , P m -i_, are matched with Q m , Q m -i, ....
  • the implementation is dynamically configurable using up to four threads.
  • a real time SLAM system may be used in combination with a real time navigation system.
  • the SLAM system may be used in combination with an obstacle detection system, such as a LIDAR- or RADAR-based obstacle detection system, a vision-based obstacle detection system, a thermal-based system, or the like. This may include detecting live obstacles, such as people, pets, or the like, such as by motion detection, thermal detection, electrical or magnetic field detection, or other mechanisms.
  • the point cloud that is established by scanning the features of an environment may be displayed, such as on a screen forming a part of the SLAM, to show a mapping of a space, which may include mapping of near field features, such as objects providing nearby reflections to the SLAM system, as well as far field features, such as items that can be scanned through spaces between or apertures in the near field features. For example, items in an adjacent hallway may be scanned through a window or door as the mapper moves through the interior of a room, because at different points in the interior of the room different outside elements can be scanned through such spaces or apertures.
  • the resulting point cloud may then comprise comprehensive mapping data of the immediate near field environment and partial mapping of far field elements that are outside the environment.
  • the SLAM system may include mapping of a space through a "picket fence" effect by identification of far-field pieces through spaces or apertures (i.e., gaps in the fence) in the near field.
  • the far field data may be used to help the system orient the SLAM as the mapper moves from space to space, such as maintaining consistent estimation of location as the mapper moves from a comprehensively mapped space (where orientation and position are well known due to the density of the point cloud) to a sparsely mapped space (such as a new room).
  • the relative density or sparseness of the point cloud can be used by the SLAM system to guide the mapper via, for example, a user interface forming a part of the SLAM, such as directing the mapper to the parts of the far field that could not be seen through the apertures from another space.
  • the point cloud map from a SLAM system can be combined with mapping from other inputs such as cameras, sensors, and the like.
  • an airplane, drone, or other airborne mobile platform may already be equipped with other distance measuring and geo-location equipment that can be used as reference data for the SLAM system (such as linking the point cloud resulting from a scan to a GPS -referenced location) or that can take reference data from a scan, such as for displaying additional scan data as an overlay on the output from the other system.
  • SLAM system such as linking the point cloud resulting from a scan to a GPS -referenced location
  • reference data from a scan such as for displaying additional scan data as an overlay on the output from the other system.
  • conventional camera output can be shown with point cloud data as an overlay, or vice versa.
  • the SLAM system can provide a point cloud that includes data indicating the reflective intensity of the return signal from each feature.
  • This reflective intensity can be used to help determine the efficacy of the signal for the system, to determine how features relate to each other, to determine surface IR reflectivity, and the like.
  • the reflective intensity can be used as a basis for manipulating the display of the point cloud in a map.
  • the SLAM system can introduce (automatically, of under user control) some degree of color contrast to highlight the reflectivity of the signal for a given feature, material, structure, or the like.
  • the system can be married with other systems for augmenting color and reflectance information.
  • one or more of the points in the point cloud may be displayed with a color corresponding to a parameter of the acquired data, such as an intensity parameter, a density parameter, a time parameter and a geospatial location parameter.
  • Colorization of the point cloud may help users understand and analyze elements or features of the environment in which the SLAM system is operating and/or elements of features of the process of acquisition of the point cloud itself.
  • a density parameter indicating the number of points acquired in a geospatial area, may be used to determine a color that represents areas where many points of data are acquired and another color where data is sparse, perhaps suggesting the presence of artifacts, rather than "real" data.
  • Color may also indicate time, such as progressing through a series of colors as the scan is undertaken, resulting in clear indication of the path by which the SLAM scan was performed. Colorization may also be undertaken for display purposes, such as to provide differentiation among different features (such as items of furniture in a space, as compared to walls), to provide aesthetic effects, to highlight areas of interest (such as highlighting a relevant piece of equipment for attention of a viewer of a scan), and many others.
  • the SLAM system can identify "shadows" (areas where the point cloud has relatively few data points from the scan) and can (such as through a user interface) highlight areas that need additional scanning. For example, such areas may blink or be rendered in a particular color in a visual interface of a SLAM system that displays the point cloud until the shadowed area is sufficiently "painted," or covered, by laser scanning.
  • Such an interface may include any indicator (visual, text-based, voice-based, or the like) to the user that highlights areas in the field that have not yet been scanned, and any such indicator may be used to get the attention of the user either directly or through an external device (such as a mobile phone of the user).
  • the system may make reference to external data of data stored on the SLAM, such as previously constructed point clouds, maps, and the like, for comparison with current scan to identify unscanned regions.
  • the methods and systems disclosed herein include a SLAM system that provides real-time positioning output at the point of work, without requiring processing or calculation by external systems in order to determine accurate position and orientation information or to generate a map that consists of point cloud data showing features of an environment based on the reflected signals from a laser scan.
  • the methods and systems disclosed herein may also include a SLAM system that provides real time positioning information without requiring post-processing of the data collected from a laser scan.
  • a SLAM system may be integrated with various external systems, such as vehicle navigation systems (such as for unmanned aerial vehicles, drones, mobile robots, unmanned underwater vehicles, self-driving vehicles, semi-automatic vehicles, and many others).
  • vehicle navigation systems such as for unmanned aerial vehicles, drones, mobile robots, unmanned underwater vehicles, self-driving vehicles, semi-automatic vehicles, and many others.
  • the SLAM system may be used to allow a vehicle to navigate within its environments, without reliance on external systems like GPS.
  • a SLAM system may determine a level of confidence as to its current estimation of position, orientation, or the like.
  • a level of confidence may be based on the density of points that are available in a scan, the orthogonality of points available in a scan, environmental geometries or other factors, or a combination thereof.
  • the level of confidence may be ascribed to position and orientation estimates at each point along the route of a scan, so that segments of the scan can be referenced as low-confidence segments, high-confidence segments, or the like. Low-confidence segments can be highlighted for additional scanning, for use of other techniques (such as making adjustments based on external data), or the like.
  • any discrepancy between the calculated end location and the starting location may be resolved by preferentially adjusting location estimates for certain segments of the scan to restore consistency of the start- and end- locations.
  • Location and position information in low-confidence segments may be preferentially adjusted as compared to high-confidence segments.
  • the SLAM system may use confidence-based error correction for closed loop scans.
  • confidence measures with respect to areas or segments of a point cloud may be used to guide a user to undertake additional scanning, such as to provide an improved SLAM scan.
  • a confidence measure can be based on a combination of density of points, orthogonality of points and the like, which can be used to guide the user to enable a better scan.
  • scan attributes such as density of points and orthogonality of points, may be determined in real time as the scan progresses.
  • the system may sense geometries of the scanning environment that are likely to result in low confidence measures. For example, long hallways with smooth walls may not present any irregularities to differentiate one scan segment from the next. In such instances, the system may assign a lower confidence measure to scan data acquired in such environments.
  • the system can use various inputs such as LIDAR, camera, and perhaps other sensors to determine diminishing confidence and guide the user through a scan with instructions (such as "slow down,” “turn left” or the like).
  • the system may display areas of lower than desired confidence to a user, such as via a user interface, while providing assistance in allowing the user to further scan the area, volume or region of low confidence.
  • a SLAM output may be fused with other content, such as outputs from cameras, outputs from other mapping technologies, and the like.
  • a SLAM scan may be conducted along with capture of an audio track, such as via a companion application (optionally a mobile application) that captures time-coded audio notes that correspond to a scan.
  • the SLAM system provides time-coding of data collection during scanning, so that the mapping system can pinpoint when and where the scan took place, including when and where the mapper took audio and/or notes.
  • the time coding can be used to locate the notes in the area of the map where they are relevant, such as by inserting data into a map or scan that can be accessed by a user, such as by clicking on an indicator on the map that audio is available.
  • other media formats may be captured and synchronized with a scan, such as photography, HD video, or the like. These can be accessed separately based on time information, or can be inserted at appropriate places in a map itself based on the time synchronization of the scan output with time information for the other media.
  • a user may use time data to go back in time and see what has changed over time, such as based on multiple scans with different time-encoded data. Scans may be further enhanced with other information, such as date- or time-stamped service record data.
  • a scan may be part of a multi-dimensional database of a scene or space, where point cloud data is associated with other data or media related to that scene, including time-based data or media.
  • calculations are maintained through a sequence of steps or segments in a manner that allows a scan to be backed up, such as to return to a given point in the scan and re-initiate at that point, rather than having to re-initiate a new scan starting at the origin.
  • a user can "unzip” or “rewind” a scan back to a point, and then recommence scanning from that point.
  • the system can maintain accurate position and location information based on the point cloud features and can maintain time information to allow sequencing with other time-based data.
  • Time-based data can also allow editing of a scan or other media to synchronize them, such as where a scan was completed over time intervals and needs to be synchronized with other media that was captured over different time intervals.
  • Data in a point cloud may be tagged with timestamps, so that data with timestamps occurring after a point in time to which a rewind is undertaken can be erased, such that the scan can re-commence from a designated point.
  • a rewind may be undertaken to a point in time and/or to a physical location, such as rewinding to a geospatial coordinate.
  • the output from a SLAM-based map can be fused with other content, such as HD video, including by colorizing the point cloud and using it as an overlay. This may include time-synchronization between the SLAM system and other media capture system. Content may be fused with video, still images of a space, a CAD model of a space, audio content captured during a scan, metadata associated with a location, or other data or media.
  • a SLAM system may be integrated with other technologies and platforms, such as tools that may be used to manipulate point clouds (e.g., CAD). This may include combining scans with features that are modeled in CAD modeling tools, rapid prototyping systems, 3D printing systems, and other systems that can use point cloud or solid model data. Scans can be provided as inputs to post-processing tools, such as colorization tools. Scans can be provided to mapping tools, such as for adding points of interest, metadata, media content, annotations, navigation information, semantic analysis to distinguish particular shapes and/or identify objects, and the like.
  • Outputs can be combined with outputs from other scanning and image-capture systems, such as ground penetrating radar, X-ray imaging, magnetic resonance imaging, computed tomography imaging, thermal imaging, photography, video, SONAR, RADAR, LIDAR and the like.
  • This may include integrating outputs of scans with displays for navigation and mapping systems, such as in-vehicle navigation systems, handheld mapping systems, mobile phone navigation systems, and others.
  • Data from scans can be used to provide position and orientation data to other systems, including X, Y and Z position information, as well as pitch, roll and yaw information.
  • the data obtained from a real time SLAM system can be used for many different purposes, including for 3D motion capture systems, for acoustics engineering applications, for biomass measurements, for aircraft construction, for archeology, for architecture, engineering and construction, for augmented reality (AR), for autonomous cars, for autonomous mobile robot applications, for cleaning and treatment, for CAD/CAM applications, for construction site management (e.g., for validation of progress), for entertainment, for exploration (space, mining, underwater and the like), for forestry (including for logging and other forestry products like maple sugar management), for franchise management and compliance (e.g., for stores and restaurants), for imaging applications for validation and compliance, for indoor location, for interior design, for inventory checking, for landscape architecture, for mapping industrial spaces for maintenance, for mapping trucking routes, for military/intelligence applications, for mobile mapping, for monitoring oil pipelines and drilling, for property evaluation and other real estate applications, for retail indoor location (such as marrying real time maps to inventory maps, and the like), for security applications, for stockpile monitoring (ore, logs, goods
  • the unit comprises hardware synchronization of the IMU, camera (vision) and the LiDAR sensor.
  • the unit may be operated in darkness or structureless environments for a duration of time.
  • the processing pipeline may be comprised of modules. In darkness, the vision module may be bypassed. In structureless environments, the LiDAR module may be bypassed or partially bypassed.
  • the IMU, Lidar and camera data are all time stamped and capable of being temporally matched and synchronized. As a result, the system can act in an automated fashion to synchronize image data and point cloud data. In some instances, color data from synchronized camera images may be used to color clod data pixels for display to the user.
  • the unit may comprise four CPU threads for scan matching and may run at, for example, 5Hz with Velodyne data.
  • the motion of the unit when operating may be relatively fast.
  • the unit may operate at angular speeds of approximately 360 degree/second and linear speeds of approximately 30m/s.
  • the unit may localize to a prior generated map.
  • the unit's software may refer to a previously built map and produce sensor poses and a new map within the framework (e.g., geospatial or other coordinates) of the old map.
  • the unit can further extend a map using localization.
  • By developing a new map in the old map frame, the new map can go further on and out of the old map.
  • branching and chaining in which an initial "backbone" scan is generated first and potentially post-processed to reduce drift and/or other errors before resuming from the map to add local details, such as side rooms in building or increased point density in a region of interest.
  • the backbone model may be generated with extra care to limit the global drift and the follow-on scans may be generated with the focus on capturing local detail. It is also possible for multiple devices to perform the detailed scanning off of the same base map for faster capture of a large region.
  • a higher global accuracy stationary device could build a base map and a mobile scanner could resume from that map and fill in details.
  • a longer range device may scan the outside and large inside areas of a building and a shorter range device may resume from that scan to add in smaller corridors and rooms and required finer details. Resuming from CAD drawings could have significant advantages for detecting differences between CAD and as-built rapidly.
  • Resuming may also provide location registered temporal data. For example, multiple scans may be taken of a construction site over time to see the progress visually. In other embodiments multiple scans of a factory may help with tracking for asset management.
  • Resuming may alternately be used to purely provide localization data within the prior map. This may be useful for guiding a robotic vehicle or localizing new sensor data, such as images, thermal maps, acoustics, etc within an existing map.
  • the unit employs relatively high CPU usage in a mapping mode and relatively low CPU usage in a localization mode, suitable for long-time localization/navigation.
  • the unit supports long-time operations by executing an internal reset every once in a while. This is advantageous as some of the values generated during internal processing increase over time. Over a long period of operation (e.g., a few days), the values may reach a limit, such as a logical or physical limit of storage for the value in a computer, causing the processing, absent a reset, to potentially fail.
  • the system may automatically flush RAM memory to improve performance.
  • the system may selectively down sample older scanned data as might be necessary when performing a real time comparison of newly acquired data with older and/or archived data.
  • the unit may support a flying application and aerial-ground map merging.
  • the unit may compute a pose output at the IMU frequency, e.g., 100Hz.
  • the software may produce maps as well as sensor poses.
  • the sensor poses tell the sensor position and pointing with respect to the map being developed.
  • High frequency and accurate pose output helps in mobile autonomy because vehicle motion control requires such data.
  • the unit further employs covariance and estimation confidence and may lock a pose when the sensor is static.
  • Figs. 17(a)- 17(b) there is illustrated exemplary and non-limiting embodiments of a SLAM. LIDAR is rotated to create a substantially hemispherical scan.
  • the spur gear reduction assembly 1704 enables the LIDAR to be offset from the motor 1708.
  • An encoder 1706 is also in line with the rotation of the LIDAR to record the orientation of the mechanism during scanning.
  • a thin section contact bearing supports the LIDAR rotation shaft. Counterweights on the LIDAR rotation plate balance the weight about the axis of rotation making the rotation smooth and constant.
  • the mechanism is designed with minimal slop and backlash to enable maintenance of a constant speed for interpolation of scan point locations.
  • a motor shaft 1710 is in physical communication with a LIDAR connector 1712.
  • SLAM enclosure 1802 there is illustrated an exemplary and non- limiting embodiment of a SLAM enclosure 1802.
  • the SLAM enclosure 1802 is depicted in a variety of views and perspectives. Dimensions are representative of an embodiment and non-limiting as the size may be similar or different, while maintaining the general character and orientation of the major components, such as the LIDAR, odometry camera, colorization camera, user interface screen, and the like.
  • the unit may employ a neck brace, shoulder brace, carrier, or other wearable element or device (not shown), such as to help an individual hold the unit while walking around.
  • the unit or a supporting element or device may include one or more stabilizing elements to reduce shaking or vibration during the scan.
  • the unit may employ a remote battery that is carried in a shoulder bag or the like to reduce the weight of the handheld unit, whereby the scanning device has an external power source.
  • the cameras and LIDAR are arranged to maximize a field of view.
  • the camera-laser arrangement poses a tradeoff. On one side, the camera blocks the laser FOV and on the other side, the laser blocks the camera. In such an arrangement, both are blocked slightly but the blocking does not significantly sacrifice the mapping quality.
  • the camera points in the same direction as the laser because the vision processing is assisted by laser data. Laser range measurements provide depth information to the image features in the processing.
  • a confidence metric representing a confidence of spatial data.
  • Such a confidence metric measurement may include, but is not limited to, number of points, distribution of points, orthogonality of points, environmental geometry, and the like.
  • One or more confidence metrics may be computed for laser data processing (e.g., scan matching) and for image processing.
  • Figs. 19(a)- 19(c) there are illustrated exemplary and non-limiting example images showing point clouds differentiated by laser match estimation confidence. While in practice, such images may be color coded, as illustrated, both the trajectory and the points are rendered as solid or dotted in the cloud based on a last confidence value at the time of recording. In the examples, dark gray is bad and light gray is good. The values are thresholded such that everything with a value >10 is solid. Through experimentation it has been found that with a Velodyne ⁇ 1 is unreliable, ⁇ 10 is less reliable, >10 is very good.
  • Using these metrics enables automated testing to resolve model issues and offline model correction such as when utilizing a loop-closure tool as discussed elsewhere herein. Use of these metrics further enables alerting the user when matches are bad and possibly auto- pausing, throwing out low confidence data, and alerting the user when scanning.
  • Fig. 19(a) illustrates a scan of a building floor performed at a relatively slow pace.
  • Fig. 19(b) illustrates a scan of the same building floor performed at a relatively quicker pace. Note the prevalence of light fray when compared to the scan acquired from a slower scan pace arising, in part, from the speed at which the scan is conducted.
  • Fig. 19(c) illustrates a display zoomed in on a potential trouble spot of relatively low confidence.
  • Fig. 20 there is illustrated an exemplary and non-limiting embodiment of scan-to-scan match confidence metric processing and an average number of visual features that track between a full laser scan and a map being built from the prior full laser scans may be computed and presented visually. This metric may present useful, but different confidence measures.
  • a laser scan confidence metric view is presented in the left frame while an average number of visual features metric is presented in the right frame for the same data. Again, dark gray line indicates lower confidence and/or fewer average number of visual features.
  • loop closure there may be employed loop closure.
  • the unit may be operated as one walks around a room, cubicle, in and out of offices, and then back to a starting point.
  • the mesh of data from the start and end point should mesh exactly.
  • the algorithms described herein greatly minimize such drift. Typical reduction is on the order of lOx versus conventional methods (0.2% v 2%). This ratio reflects the error in distance between the start point and end point divided by the total distance traversed during the loop.
  • the software recognizes that it is back to a starting point and it can relock to the origin. Once done, one may take the variation and spread it over all of the collected data.
  • one may lock in certain point cloud data where a confidence metric indicates that the data confidence was poor and one may apply the adjustments to the areas with low confidence.
  • the system may employ both explicit and implicit loop closure.
  • a user may indicate, such as via a user interface forming a part of the SLAM, that a loop is to be closed.
  • This explicit loop closure may result in the SLAM executing software that operates to match recently scanned data to data acquired at the beginning of the loop in order to snap the beginning and end acquired data together and close the loop.
  • the system may perform implicit loop closure. In such instances, the system may operate in an automated fashion to recognize that the system is actively rescanning a location that that comprises a point or region of origin for the scan loop.
  • multi-loop confidence- based loop closure there may be performed multi-loop confidence- based loop closure.
  • structural information may be derived from the attribution of a scanned element, i.e., floors are flat, corridors are straight, etc.
  • each pixel in the camera can be mapped to a unique LIDAR pixel. For example, one may take color data from a pixel in the colorization camera corresponding to LIDAR data in the point cloud, and add the color data to the LIDAR data.
  • the unit may employ a sequential, multi-layer processing pipeline, solving for motion from coarse to fine.
  • the prior coarser result is used as an initial guess to the optimization problem.
  • the steps in the pipeline are:
  • IMU mechanization for motion prediction which provides high frequency updates (on order of 200 Hz), but is subject to high levels of drift.
  • this estimate is refined by a visual-inertial odometry optimization at the frame rate of the cameras (30-40Hz), the optimization problem uses the IMU motion estimate as an initial guess of pose change and adjusts that pose change in an attempt to minimize residual squared errors in motion between several features tracked from the current camera frame to a key frame.
  • this estimate is further refined by a laser odometry optimization at a lower rate determined by the "scan frame" rate.
  • Scan data comes in continuously, and software segments that data into frames, similar to image frames, at a regular rate, currently that rate is the time it takes for one rotation of the LIDAR rotary mechanism to make each scan frame a full hemisphere of data. That data is stitched together using visual-inertial estimates of position change as the points within the same scan frame are gathered.
  • the visual odometry estimate is taken as an initial guess and the optimization attempts to reduce residual error in tracked features in the current scan frame matched to the prior scan frame.
  • the current scan frame is matched to the entire map so far.
  • the laser odometry estimate is taken as the initial guess and the optimization minimizes residual squared errors between features in the current scan frame and features in the map so far.
  • the resulting system enables high-frequency, low-latency ego-motion estimation, along with dense, accurate 3D map registration. Further, the system is capable of handling sensor degradation by automatic reconfiguration bypassing failure modules since each step can correct errors in the prior step. Therefore, it can operate in the presence of highly dynamic motion as well as in dark, texture-less, and structure-less environments. During experiments, the system demonstrates 0.22% of relative position drift over 9.3km of navigation and robustness with respect to running, jumping and even highway speed driving (up to 33m/s).
  • Visual feature optimization / and /o depth The software may attempt to determine a depth of tracked visual features, first by attempting to associate them with the laser data and secondly by attempting to triangulate depth between camera frames. The feature optimization software may then utilize all features with two different error calculations, one for features with depth and one for features without depth.
  • Laser feature determination The software may extract laser scan features as the scan line data comes in rather than in the entire scan frame. This is much easier and is done by looking at the smoothness at each point, which is defined by the relative distance between that point and the K nearest points on either side of that point then labeling the smoothest points as planar features and the sharpest as edge features. It also allows for the deletion of some points that may be bad features.
  • Map matching and voxelization A part of how the laser matching works in realtime is how the map and feature data is stored. Tracking the processor load of this stored data is critical to long term scanning and selectively voxelizing, or down-sampling into three- dimensional basic units in order to minimize the data stored while keeping what is needed for accurate matching. Adjusting the voxel sizes, or basic units, of this down-sampling on the fly based on processor load may improve the ability to maintaining real-time performance in large maps.
  • the software may be setup in such a way that it can utilize parallel processing to maintain real-time performance if data comes in faster than the processor can handle it. This is more relevant with faster point/second LIDARS like the velodyne.
  • Each optimization step in this process may provide information on the confidence in its own results.
  • the following can be evaluated to provide a measure of confidence in results: the remaining residual squared error after the optimization, the number of features tracked between frames, and the like.
  • the user may be presented a down scaled, (e.g., sub sampled) version of the multi- spectral model being prepared with data being acquired by the device.
  • each measured 3cm x 3cm x 3cm cube of model data may be represented in the scaled down version presented on the user interface as a single pixel.
  • the pixel selected for display may be the pixel that is closest to the center of the cube.
  • a representative down-scaled display being generated during operation of the SLAM is shown below.
  • the decision to display a single pixel in a volume represents a binary result indicative of either the presence of one or more points in a point cloud occupying a spatial cube of defined dimensions or the absence of any such points.
  • the selected pixel may be attributed, such as with a value indicating the number of pixels inside the defined cube represented by the selected pixel.
  • This attribute may be utilized when displaying the sub sampled point cloud such as by displaying each selected pixel utilizing color and/or intensity to reflect the value of the attribute.
  • a visual frame comprises a single 2D color image from the colorization camera.
  • a LIDAR segment comprises s full 360 degree revolution of the LIDAR scanner.
  • the visual frame and LIDAR segment are synchronized so that they can be combined and aligned with the existing model data based on the unit positional data captured from the IMU and related sensors, such as the odometry (e.g., a high speed black/white) camera.
  • the odometry e.g., a high speed black/white
  • Problems giving rise to lower confidence metrics include, but are not limited to, reflections off of glossy walls, glass, transparent glass and narrow hallways.
  • a user of the unit may pause and resume a scan such as by, for example, hitting a pause button and/or requesting a rewind to a point that is a predetermined or requested number of seconds in the past.
  • rewinding during a scan may proceed as follows.
  • the user of the system indicates a desire to rewind. This may be achieved through the manipulation of user interface forming a part of the SLAM.
  • the system deletes or otherwise removes a portion of scanned data points corresponding to a duration of time. As all scanned data points are time stamped, the system can effectively remove data points after a predetermined time, thus, "rewinding" back to a previous point in a scan.
  • the system may provide the user with a display of an image recorded at the predetermined point in time while displaying the scanned point cloud rewound to the predetermined point in time.
  • the image acts as a guide to help the user of the system reorient the SLAM into a position closely matching the orientation and pose of the SLAM at the previous predetermined point in time.
  • the user may indicate a desire to resume scanning such as by engaging a "Go" button on a user interface of the SLAM.
  • the SLAM may proceed execute a processing pipeline utilizing newly scanned data to form an initial estimation of the SLAMs position and orientation.
  • the SLAM may not add new data to the scan but, rather, may use the newly scanned data to determine and display an instantaneous confidence level of the user's position as well as a visual representation of the extent to which newly acquired data corresponds to the previous scan data.
  • scanning may continue.
  • this ability to rewind is enabled, in part, by the data being stored.
  • One may estimate how many points are brought in per second and then estimate how much to "rewind".
  • the unit may inform the user where he was x seconds a go and allow the user to move to that location and take a few scans to confirm that the user is at the appropriate place. For example, the user may be told an approximate place to go to (or the user indicate where they want to restart). If the user is close enough, the unit may figure it out where the user is and tell the user if they are close enough.
  • the unit may operate in transitions between spaces. For example, If a user walks very quickly through a narrow doorway there may not be enough data and time to determine the user's place in the new space. Specifically, in this example, the boundaries of a door frame may, prior to proceeding through it, block the LIDAR from imaging a portion of the environment beyond the door sufficient to establish a user's location. One option is to detect this lowering of confidence metric and signal to the operator to modify his behavior upon approaching a narrow passage to slow down, such as by a flashing a visual indicator or a changing the color of the screen, and the like.
  • the SLAM unit 2100 may include a timing server to generate multiple signals derived from the IMU's 2106 pulse -per-second (PPS) signal. The generated signals may be used to synchronize the data collected from the different sensors in the unit.
  • a microcontroller 2102 may be used to generate the signals and communicate with the CPU 2104.
  • the quadrature decoder 2108 may either be built into the microcontroller or on an external IC.
  • the IMU 2206 supplies a rising edge PPS signal that is used to generate the timing pulses for other parts of the system.
  • the camera may receive three signals generated from the IMU PPS signal including one rising edge signal as described above and two falling edge signals, GPIOl (lasting one frame) and GPI02 (lasting two frames as illustrated with reference to Fig. 22.
  • each camera receives a trigger signal synchronized with the IMU PPS having a high frame rate of approximately 30 Hz or 40 Hz and a high resolution of approximately 0.5 Hz - 5 Hz.
  • Each IMS PPS pulse may zero a counter internal to the microcontroller 2202.
  • LIDAR' s synchronous output may trigger the following events:
  • the encoder and the counter values may be saved together and sent to the CPU. This may happen every 40 Hz, dictated by the LIDAR synchronous output as illustrated with reference to Fig. 23.
  • An alternate time synchronization technique may include IMU based pulse-per- second synchronization that facilitates synchronizing the sensors and the computer processor. An exemplary and non-limiting embodiment of this type of synchronization is depicted with reference to Fig. 24.
  • the IMU 2400 may be configured to send a Pulse Per Second (PPS) signal 2406 to a LIDAR 2402. Every time a PPS is sent, the computer 2404 is notified by recognizing a flag in the IMU data stream. Then, the computer 2404 follows up and sends a time string to the LIDAR 2402. The LIDAR 2402 synchronizes to the PPS 2406 and encodes time stamps in the LIDAR data stream based on the received time strings.
  • PPS Pulse Per Second
  • the computer 2404 Upon receiving the first PPS 2406, the computer 2404 records its system time. Starting from the second PPS, the computer 2404 increases the recorded time by one second, sends the resulting time string to the LIDAR 2402, and then corrects its own system time to track the PPS 2506.
  • the IMU 2400 functions as the time server, while the initial time is obtained from the computer system time.
  • the IMU 2400 data stream is associated with time stamps based on its own clock, and initialized with the computer system time when the first PPS 2406 is sent. Therefore, the IMU2400, LIDAR 2402, and computer 2404 are all time synchronized.
  • the LIDAR 2402 may be a Velodyne LIDAR.
  • the unit includes a COM express board and a single button interface for scanning.
  • the process IMU, vision and laser data sensors may be coupled.
  • the unit may work in darkness or structureless environments for long periods of time.
  • four CPU threads may be employed for scan matching, each running at 5Hz with Velodyne data.
  • motion of the unit may be fast and the unit may localize to a prior map and can extend a map using localization.
  • the unit exhibits relatively high CPU usage in mapping mode and relatively low CPU usage in localization mode thus rendering it suitable for long-time.
  • ground-based mapping is not necessarily prone to limitations of space or time.
  • a mapping device carried by a ground vehicle is suitable for mapping in large scale and can move at a high speed.
  • a tight area can be mapped in a hand-held deployment.
  • ground-based mapping is limited by the sensor's altitude making it difficult to realize a top-down looking configuration. As illustrated in Fig. 25, the ground-based experiment produces a detailed map of the surroundings of a building, while the roof has to be mapped from the air. If a small aerial vehicle is used, aerial mapping is limited by time due to the short lifespan of batteries. Space also needs to be open enough for aerial vehicles to operate safely.
  • the collaborative mapping as described herein may utilize a laser scanner, a camera, and a low-grade IMU to process data through multi-layer optimization.
  • the resulting motion estimates may be at a high rate ( ⁇ 200Hz) with a low drift (typically ⁇ 0.1% of the distance traveled).
  • the high-accuracy processing pipeline described herein may be utilized to merge maps generated from the ground with maps generated from the air in real-time or near realtime. This is achieved, in part, by localization of one output from a ground derived map with respect to an output from an air derived map.
  • the method disclosed herein fulfills collaborative mapping, it further reduces the complexity of aerial deployments.
  • a ground-based map flight paths are defined and an aerial vehicle conducts mapping in autonomous missions.
  • the aerial vehicle is able to accomplish challenging flight tasks autonomously.
  • the processing software is not necessarily limited to a particular sensor configuration.
  • the sensor pack 2601 is comprised of a laser scanner 2603 generating 0.3 million points/second, a camera 2605 at 640 x 360 pixels resolution and 50Hz frame rate, and a low-grade IMU 2607 at 200Hz.
  • An onboard computer processes data from the sensors in real-time for ego-motion estimation and mapping.
  • Fig. 26(b) and Fig. 26(c) illustrate the sensor field of view. An overlap is shared by the laser and camera, with which, the processing software associates depth information from the laser to image features as described more fully below.
  • the software processes data from a range sensor such as a laser scanner, a camera, and an inertial sensor.
  • a range sensor such as a laser scanner, a camera, and an inertial sensor.
  • the methods and systems described herein parse the problem as multiple small problems, solve them sequentially in a coarse-to-fine manner.
  • Fig. 27 illustrates a block diagram of the software system. In such a system, modules in the front conduct light processing, ensuring high- frequency motion estimation robust to aggressive motion. Modules in the back take sufficient processing, run at low frequencies to warrant accuracy of the resulting motion estimates and maps.
  • the software starts with IMU data processing 2701.
  • This module runs at the IMU frequency to predict the motion based on IMU mechanization.
  • the result is further processed by a visual-inertial coupled module 2703.
  • the module 2703 tracks distinctive image features through the image sequence and solves for the motion in an optimization problem.
  • laser range measurements are registered on a depthmap, with which, depth information is associated to the tracked image features. Since the sensor pack contains a single camera, depth from the laser helps solve scale ambiguity during motion estimation.
  • the estimated motion is used to register laser scans locally.
  • these scans are matched to further refine the motion estimates.
  • the matched scans are registered on a map while scans are matched to the map.
  • scan matching utilizes multiple CPU threads in parallel.
  • the map is stored in voxels to accelerate point query during scan matching. Because the motion is estimated at different frequencies, a fourth module 2707 in the system takes these motion estimates for integration. The output holds both high accuracy and low latency beneficial for vehicle control.
  • the modularized system also ensures robustness with respect to sensor degradation, by selecting "healthy" modes of the sensors when forming the final solution. For example, when a camera is in a low-light or texture-less environment such as pointing to a clean and white wall, or a laser is in a symmetric or extruded environment such as a long and straight corridor, processing typically fails to generate valid motion estimates.
  • the system may automatically determine a degraded subspace in the problem state space. When degradation happens, the system only solves the problem partially in the well-conditioned subspace of each module. The result is that the "healthy" parts are combined to produce the final, valid motion estimates.
  • the method described above can be extended to utilize the map for localization. This is accomplished using a scan matching method.
  • the method extracts two types of geometric features, specifically, points on edges and planar surfaces, based on the curvature in local scans. Feature points are matched to the map. An edge point is matched to an edge line segment, and a planar point is matched to a local planar patch. On the map, the edge line segments and local planar patches are determined by examining the eigenvalues and eigenvectors associated with local point clusters.
  • the map is stored in voxels to accelerate processing.
  • the localization solves an optimization problem minimizing the overall distances between the feature points and their correspondences. Due to the fact that the high accuracy odometry estimation is used to provide initial guess to the localization, the optimization usually converges in 2-3 iterations.
  • the localization does not necessarily process individual scans but, rather, stacks a number of scans for batch processing. Thanks to the high-accuracy odometry estimation, scans are registered precisely in a local coordinate frame where drift is negligible over a short period of time (e.g., a few seconds).
  • Fig. 28 (8.4) where Fig. 28(a) is a single scan that is matched in the previous section (scan matching executes at 5Hz), and Fig. 28(b) shows stacked scans over two seconds, which are matched during localization (scan matching runs at 0.5Hz).
  • Fig. 28(a) is a single scan that is matched in the previous section (scan matching executes at 5Hz)
  • Fig. 28(b) shows stacked scans over two seconds, which are matched during localization (scan matching runs at 0.5Hz).
  • the stacked scans contain significantly more structural details, contributing to the localization accuracy and robustness with respect to environmental changes.
  • the localization is compared to a particle filter based implementation.
  • the odometry estimation provides the motion model to the particle filter. It uses a number of 50 particles. At each update step, the particles are resampled based on low-variance resampling. Comparison results are shown in Fig. 29 and Table 8.1.
  • errors are defined as the absolute distances from localized scans to the map.
  • the methods and systems described herein choose a number of planar surfaces and use the distances between points in localized scans to the corresponding planar patches on the map.
  • Fig. 29 illustrates an exemplary and non-limiting embodiment of an error distribution. When running the particle filter at the same frequency as the described method (0.5Hz), the resulting error is five times as large.
  • Fig. 30(a) shows the map built and sensor trajectory.
  • Fig. 30(b) is a single scan. In this scenario, the scan contains sufficient structural information. When bypassing the camera processing module, the system produces the same trajectory as the full pipeline.
  • the methods and systems described herein run another test with the sensor pack tilted vertically down toward the ground. The results are shown in Fig. 31. In this scenario, structural information in a scan is much sparser as shown in Fig. 31(b)). The processing fails without usage of the camera and succeeds with the full pipeline. The results indicate the camera is critical for high-altitude flights where tilting of the sensor pack is required.
  • FIG. 32 there is illustrated an exemplary and non-limiting embodiment wherein the sensor pack is held by an operator walking through a circle at l-2m/s speed with an overall traveling distance of 410m.
  • Fig. 32(a) shows the resulting map and sensor trajectory with a horizontally orientated sensor configuration. The sensor is started and stopped at the same position. The test produces 0.18m of drift through the path, resulting in 0.04% of relative position error in comparison to the distance traveled. Then, the operator repeats the path with two sensor packs held at 45° and 90° angles, respectively. The resulting sensor trajectories are shown in Fig. 32(b).
  • FIG. 33 An exemplary and non-limiting embodiment of a drone platform 3301 is illustrated at Fig. 33.
  • the aircraft weighs approximately 6.8kg (including batteries) and may carry a maximum of 4.2kg payload.
  • the sensor/computer pack is mounted to the bottom of the aircraft, weighting 1.7kg.
  • the bottom right of the figure shows the remote controller.
  • the remote controller is operated by a safety pilot to override the autonomy if necessary.
  • the aircraft is built with a GPS receiver (on top of the aircraft). GPS data is not necessarily used in mapping or autonomous.
  • FIG. 25 In the first collaborative mapping experiment, an operator holds the sensor pack and walks around a building. Results are shown in Fig. 25. In Fig.
  • the ground-based mapping covers surroundings of the building in detail, conducted at l-2m/s over 914m of travel. As expected, the roof of the building is empty on the map.
  • the drone is teleoperated to fly over the building.
  • the flight is conducted at 2-3m/s with a traveling distance of 269m.
  • the processing uses localization w.r.t. the map in Fig. 25(a). That way, the aerial map is merged with the ground-based map (white points).
  • the take-off position of the drone is determined on the map.
  • the sensor starting pose for the aerial mapping is known, and from which, the localization starts.
  • Fig. 34 presents the aerial and ground-based sensor trajectories, in top-down and side views.
  • a ground-based map is built first by handheld mapping at l-2m/s for 672m of travel around the flight area.
  • the map and sensor trajectory are shown in Fig. 35(a).
  • way-points are defined and the drone follows the way-points to conduct aerial mapping.
  • the curve is the flight path
  • the large points on the curve are the way-points
  • the points form the aerial map.
  • the drone takes off inside a shed on the left side of the figure, flies across the site and passes through another shed on the right side, then returns to the first shed to land.
  • Fig. 35(c) and Fig. 35(d) are two images taken by an onboard camera when the drone flies toward the shed on the right and is about to enter the shed.
  • Fig. 35(e) shows the estimated speed during the mission.
  • the ground-based mapping involves an off -road vehicle driven at lOm/s from the left end to the right end, over 1463m of travel. With the ground-based map and way-points, the autonomous flight crosses the site. Upon take-off, the drone ascends to 20m high above the ground at 15m/s. Then, it descends to 2m above the ground to fly through a line of trees at lOm/s. The flight path is 1118m long as indicated by the curve 3601 in Fig. 36(b). Two images are taken as the drone flies high above the trees (see Fig. 36(c)) and low underneath the trees (see Fig. 36(d)).
  • global positioning data such as from a GPS may be incorporated into the processing pipeline.
  • Global positioning data can be helpful to cancel ego-motion estimation drift over a long distance of travel and register maps in a global coordinate frame.
  • GPS data may be recorded simultaneously with mapping activity. As the system moves there is some level of drift that causes an error to grow over distance. One may typically experience only 0.2% drift rate but when traveling 1000 meters that is still 2 meters for every 1000 meters of travel. At 10km this grows to 20 meters, etc. Without closing the loop (in the traditional sense of coming back to the beginning of the route) this error cannot be corrected.
  • the system can know where it is and correct the current position estimate. While this is typically done in a post-processing effort, the present system is able to accomplish such a correction in real-time or near real-time.
  • GPS provides a method by which one may close the loop.
  • GPS has some amount of error as well, but it is usually consistent in a given area and many GPS systems today can provide better than 30cm accuracy in position X and Y on the surface. Other more expensive and sophisticated systems can provide cm level positioning.
  • GPS provides important capabilities: 1. The location of the point cloud on the planet. 2. the ability to use the course-corrected information to align and "fix" the map so that the map becomes even more accurate since one knows one's position and any data taken at that position may now be referenced to the series of GPS points that are also collected. 3. The ability for the system to act as an IMU when GPS is lost.
  • dynamic vision sensors may be utilized to further improve estimation robustness with respect to aggressive motion.
  • a dynamic vision sensor reports data only on pixels with illumination changes, delivering both a high rate and a low latency.
  • This high rate (typically defined as more than approximately 10Hz) may provide rapid information quickly to the ego-motion and estimation system thus improving values for localization and, subsequently, mapping. If the system is able to capture more data with fewer delays, the system will be more accurate, and more robust.
  • the features that are identified and tracked by the dynamic vision sensor enable better estimates since more features, and faster updates enable more accurate tracking and motion estimation.
  • Direct methods may be used to realize image matching with a dynamic vision sensor for ego-motion estimation. Specifically, direct methods match sequential images for feature tracking from image to image. In contrast, the feature tracking method disclosed herein is superior to the direct method.
  • parallel processing may be implemented to execute on a general purpose GPU or FPGA and therefore enable data processing in larger amount and higher frequencies.
  • Parallel architectures may take the form of multiple cores, thread, processors or even specialized forms such as Graphics Processing Units (GPUs).
  • GPUs Graphics Processing Units
  • loop closures may be introduced to remove ego-motion estimation drift by global smoothing.
  • Another way may be to use some other measure of error during the traverse and applying corrections for only those places where the measure of error is high.
  • the covariance matrix provides a convenient metric for map quality and may be used to proactively distribute this error over the full traverse.
  • locations with low-quality matching that show up in the covariance matrix could be used to unevenly spread the error across the traverse in accordance with the proportion of the error over that traverse. This will act to correct the error in the precise areas where low-quality values were associated with particular locations.
  • a sensing device such as a commonly-carried cellular phone, smart phone, or other device that picks up, for example, a variety of wireless signals and uses a camera or other sensors to provide visual or geometric information of the environment such as may be utilized to produce a 3D model or map of said environment.
  • This calculation of position or, more generally, pose can be combined with data from the other sensors to produce maps of any of the sensed qualities in multiple dimensions, including the same dimensions being captured over time to, for example, model time- varying qualities by location.
  • This combined data may be used to model time-varying qualities, and the like.
  • Time is a useful aspect of dynamic data and adds another dimension of interest. Variations can be just as important. One may either provide time-based measurements assuming some periodicity to the signals or extract salient information about
  • a data gathering device such as a SLAM
  • a data gathering device is moved through an environment to create a map recording both point cloud data and sensor pose information.
  • non-geospatial sensor data is recorded and time information is used to synchronized the map position with the sensor information.
  • an overall map that includes geometry and additional non-geospatial sensor information is created.
  • Non-geospatial sensors may be one or more of RF detection, thermal, visual, acoustic, electrical signal, magnetic, radioactive, or other sensing modalities.
  • Fig. 37B there is illustrated an exemplary and non-limiting embodiment of deriving sensor position from the map of Fig. 37A.
  • the map embedded with non-geospatial sensor data is retrieved.
  • newly acquired non-geospatial sensor data is collected from a device.
  • localization software matches the received sensor readings with one or more positions in the map that exhibits similar values corresponding to the sensor readings.
  • a "heat map” refers to a map comprised of a plurality of non-spatial attributes each having a value indicative of an intensity of measurement.
  • a heat map may comprise non-spatial attribute values for a region or sub-region of the extent comprising the map.
  • the heat map may be formed of a plurality of discreet non-spatial attribute values from which additional heat map values may be interpolated. Location-based examples and sensing modalities may include:
  • Light level mapping to capture spectral power distribution or photometric values from which a variety of other measurements can be calculated.
  • Temporal changes - If done at different times of the day or over a season or less or greater frequency than a temporal and spatial map of such values can be created to provide useful time-varying information. Measurements done at different times of the day or over a season or less or greater frequency than a temporal and spatial map of such values can be created to provide useful time-varying information.
  • Audio measurements in 3D such as to map the acoustics of a performing arts hall or other arena where sound quality matters.
  • FIG. 38 there is illustrated an exemplary and non- limiting embodiment of a heat map style representation 3800 of sensed information superimposed on floor plan geometry.
  • Such data could represent one or more of many sensing modalities and/or sensor attributes.
  • contour lines reflect areas of similar attribute value.
  • the colocation of a localization means, such as a SLAM device with that of an additional sensor, or a wireless communication device to provide another measurement may provide an accurate map of the sensed quantity or quantities of the information including the information collected by the additional sensor in space and/or time.
  • an accurate map may be made with a sensor set, such as a SLAM device and a mobile phone or other signal detection device as described above and elsewhere herein, that may be impractical to deploy frequently.
  • This set may be too expensive, too large, too heavy, or too unwieldy to use or be limited in duration and accuracy.
  • a more sophisticated mapping system to create an initial accurate map that may include multiple modes of sensing (e.g., SLAM plus WiFi signal, heat, radioactivity, sunlight, and others), a lesser system can later be enabled to facilitate determination of a position that is more accurate than the lesser system by itself would provide.
  • a robot or a person might carry a mapping and localization device, such as a SLAM device, to produce a registered 3D point cloud that can later be converted into a map of the 3D geometry of the environment.
  • a mapping and localization device such as a SLAM device
  • Other signals such as RF sources such as Wi-Fi and Bluetooth can be integrated into that map and database so that when a device that uses one or more of these other signals is introduced into the environment, such as at an unknown position, the integrated map and database may be used to provide a position fix for the newly introduced device.
  • the density and quality of the resultant maps can be improved by interpolating between measured points within a given sensor type (e.g., RF) or across sensor types (e.g., RF and magnetic) to provide a more continuous representation of the various different sensor data in the environment.
  • a given sensor type e.g., RF
  • sensor types e.g., RF and magnetic
  • Such an interpolation may or may not be linear as some sensed quantities may fall off as a power function with distance.
  • Magnetic fields are one example where the level may drop off exponentially.
  • the use of this multi-modal data set may include determining placement of RF or other signaling devices to ensure adequate coverage.
  • One example commercial need is the placement of transmitting and receiving micro antennas for RF sources including, but not limited to, 5G, LTE, Wi-Fi, or Bluetooth to provide adequate and complete coverage. In essence, this may be the opposite of
  • LTE Long-Term Evolution
  • This process can also be used to evaluate the reception of signals from such devices to ensure they provide full coverage or specialized coverage of a site. This is an important application because if this placement is not optimized then either gaps exist, or there is inefficient overlapping coverage.
  • Wi-Fi one such RF signal
  • a signal propagation model using signal strength may be used as a basis for determining position.
  • An exemplary approach to generating and using a multi-modal map of an environment includes determining position.
  • a user may create real-time and interactive 3D models using a device such as a SLAM device while simultaneously gathering information on signal strength from a variety of sources such as Bluetooth, Wi-Fi, cell tower, and the like.
  • a device such as a SLAM device
  • the 3D model generated by the SLAM device may be used to determine signal strength, which may translate into position of RF or other beacon sources.
  • smartphone or other cameras may also be used to help identify places within the environment.
  • the image and subsequent analysis may be used to further determine the position and even be used to layer graphics and identifying information such as direction of travel, places of interest, and directions. Examples include: (i) finding one's position; (ii) formulating a path to a destination, such as an exit, fire extinguisher, human rescue, other interior location (e.g., another store in a mall); (iii) identifying a place of interest; (iv) forming a basis for augmented reality of the image being captured through the phone camera or the like.
  • Indoor positioning may be based on signal strength from several sources including, but not limited to, Wi-Fi.
  • One approach includes using localization algorithms to find a position that corresponds to a particular set of signals.
  • Another approach may include using those algorithms based on a fingerprinted position. The former uses the signal propagation model to convert measured signal strength into distance information. Then, the target coordinates can be calculated according to the distance between the moving target and the multiple access points with known coordinates.
  • Wi-Fi signals are susceptible to environmental factors including walls, windows, equipment, and windows, even people.
  • the attenuation level is related to the shape, size, and material of the materials. Therefore, for indoor environments with complex structure, a generalized signal propagation model may not directly and accurately correspond between the actual distance and the signal strength, even given a good understanding of the position of the Wi-Fi signal sources (e.g., the routers, repeaters, and the like).
  • Fingerprint localization consists of offline location-fingerprint database generation and online positioning. Typically, to build the database, some appointed locations in the building are sampled for signal strength. A collection of Wi-Fi signals and their intensity will be recorded and considered as signal fingerprint.
  • signals may be normalized and statistical measures including regression, Chi-squared, or Bayesian formulations may all be used to classify data, determining if sensed quantities are independent or not.
  • Other classifiers include neural networks (CNN, ANN, etc) and this would require training sets to successfully classify inputs and sensor reading. Over time certain signals that show less variation may be accorded greater confidence and thus greater confidence implies greater trust in those values and resultant calculations of position.
  • the collection and fingerprinting is done in coordination with the high-resolution mapping (e.g., SLAM based) so that the fingerprints can be aligned to the map.
  • signal information is collected around the position to be localized and used to generate a currently sensed fingerprint.
  • These currently sensed fingerprints may be compared with fingerprints in the offline database by, for example, determining a best match to the fingerprints in the offline database.
  • the corresponding map position matching the position whose fingerprint can attain the best match is chosen as the estimated position.
  • the Wi-Fi fingerprint- based positioning technology needs to make a fingerprint database, or map, at the early stage, it may effectively avoid the influence of building structure.
  • fingerprint-based methods do not require that Wi-Fi access points are known beforehand. Therefore, it has a higher practicability.
  • One problem that can be simplified using the methods described herein include determining how best to provide good LTE or the like signal coverage indoors.
  • Uses of this data could also help with placement of ultra-high bandwidth coverage, such as 5G networks to provide high bandwidth data.
  • Some applications could greatly benefit from these systems that have bandwidths that are up to three orders of magnitude greater bandwidth than Wi-Fi rates.
  • medical imaging and the like could use high-resolution 4K, 8K, and the like cameras, microphones, EKG machines, ultrasound machines, and the like. Ensuring that bandwidth coverage for such devices, while ensuring lower bandwidth coverage elsewhere in an environment may allow real-time remote monitoring and control.
  • Another use may include providing a localization system utilizing a combination of active and passive sensing modalities to measures local geometry to provide a map that is based on the simultaneous gathering of RF sources that also provides readings of signal strength of RF sources in the environment.
  • An exemplary multi-modal sensing system may use one or more of each of the following: LIDAR, camera, depth camera, infrared camera, IMU, ultrasonic, Wi-Fi, Bluetooth, temperature sensor, barometer.
  • the LIDAR may be a ID (single point distance) or a 2D (line) or a 3D LIDAR with mechanical or solid-state means to control the beam.
  • the camera may operate at a high or low frame rate.
  • the camera may be high (e.g., 4K) or low resolution.
  • the depth camera may have long or short range.
  • Sensors such as cameras and LIDAR produce signals that can vary considerably in capability and accuracy. Range measurement for example, in some systems can range from meters to sub-millimeters depending on the design, construction, and calibration of these units. Very often there is a trade-off between accuracy and cost.
  • Other aspects of the methods and systems of multi-modal SLAM-based 3D mapping with additional sensing may include a user interface to facilitate measurements between the means.
  • a user interface tool that allows the measurements by taking the spatial median value of point cloud sub-groups such as walls and providing a measure to another such point cloud structure. The mean-to-mean distance is thus more accurate than any one set of measurements between the two surfaces.
  • the user interface may take the form of a measurement by drawing a line or a curve.
  • the software may then cluster the overlaid points and provide a single line corresponding to the median value of the line.
  • Another aspect may include combining low-resolution with high-resolution to produce high quality maps.
  • combinations of maps from multiple mapping units may be combined by wireless transmission or file transfers so many mappers update a complete map in real-time.
  • multiple consumers may be led towards each other to find someone else or for two people to efficiently find each other in the shortest time possible.
  • paths may be stored for first responders to find people quickly.
  • the methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor.
  • the present disclosure may be implemented as a method on the machine, as a system or apparatus as part of or in relation to the machine, or as a computer program product embodied in a computer readable medium executing on one or more of the machines.
  • the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform.
  • a processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like.
  • the processor may be or may include a signal processor, digital processor, embedded processor, microprocessor or any variant such as a co-processor (math co-processor, graphic co-processor, communication co-processor and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon.
  • the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application.
  • methods, program codes, program instructions and the like described herein may be implemented in one or more thread.
  • the thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code.
  • the processor may include non- transitory memory that stores methods, codes, instructions and programs as described herein and elsewhere.
  • the processor may access a non-transitory storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere.
  • the storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.
  • a processor may include one or more cores that may enhance speed and performance of a multiprocessor.
  • the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).
  • the methods and systems described herein may be deployed in part or in whole through a machine that executes computer software on a server, client, firewall, gateway, hub, router, or other such computer and/or networking hardware.
  • the software program may be associated with a server that may include a file server, print server, domain server, internet server, intranet server, cloud server, and other variants such as secondary server, host server, distributed server and the like.
  • the server may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like.
  • the methods, programs, or codes as described herein and elsewhere may be executed by the server.
  • other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.
  • the server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, and the like. Additionally, this coupling and/or connection may facilitate remote execution of program across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more location without deviating from the scope of the disclosure.
  • any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code and/or instructions.
  • a central repository may provide program instructions to be executed on different devices.
  • the remote repository may act as a storage medium for program code, instructions, and programs.
  • the software program may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client and the like.
  • the client may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like.
  • the methods, programs, or codes as described herein and elsewhere may be executed by the client.
  • other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.
  • the client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers and the like. Additionally, this coupling and/or connection may facilitate remote execution of program across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more location without deviating from the scope of the disclosure.
  • any of the devices attached to the client through an interface may include at least one storage medium capable of storing methods, programs, applications, code and/or instructions.
  • a central repository may provide program instructions to be executed on different devices.
  • the remote repository may act as a storage medium for program code, instructions, and programs.
  • the methods and systems described herein may be deployed in part or in whole through network infrastructures.
  • the network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers,
  • the computing and/or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM and the like.
  • a storage medium such as flash memory, buffer, stack, RAM, ROM and the like.
  • the processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructural elements.
  • the methods and systems described herein may be adapted for use with any kind of private, community, or hybrid cloud computing network or cloud computing environment, including those which involve features of software as a service (SaaS), platform as a service (PaaS), and/or infrastructure as a service (IaaS).
  • the methods, program codes, and instructions described herein and elsewhere may be implemented on a cellular network has sender-controlled contact media content item multiple cells.
  • the cellular network may either be frequency division multiple access (FDMA) network or code division multiple access (CDMA) network.
  • FDMA frequency division multiple access
  • CDMA code division multiple access
  • the cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like.
  • the cell network may be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.
  • the methods, program codes, and instructions described herein and elsewhere may be implemented on or through mobile devices.
  • the mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players and the like. These devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices.
  • the computing devices associated with mobile devices may be enabled to execute program codes, methods, and instructions stored thereon. Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices.
  • the mobile devices may communicate with base stations interfaced with servers and configured to execute program codes.
  • the mobile devices may communicate on a peer-to-peer network, mesh network, or other
  • the program code may be stored on the storage medium associated with the server and executed by a computing device embedded within the server.
  • the base station may include a computing device and a storage medium.
  • the storage device may store program codes and instructions executed by the computing devices associated with the base station.
  • the computer software, program codes, and/or instructions may be stored and/or accessed on machine readable media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time;
  • RAM random access memory
  • mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types
  • processor registers cache memory, volatile memory, non- volatile memory
  • optical storage such as CD, DVD
  • removable media such as flash memory (e.g. USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like
  • other computer memory such as dynamic memory, static memory, read/write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.
  • the methods and systems described herein may transform physical and/or or intangible items from one state to another.
  • the methods and systems described herein may also transform data representing physical and/or intangible items from one state to another.
  • machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices has sender-controlled contact media content item artificial intelligence, computing devices, networking equipment, servers, routers and the like.
  • the elements depicted in the flow chart and block diagrams or any other logical component may be implemented on a machine capable of executing program instructions.
  • microprocessors microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory.
  • the processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine-readable medium.
  • the computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.
  • a structured programming language such as C
  • an object oriented programming language such as C++
  • any other high-level or low-level programming language including assembly languages, hardware description languages, and database programming languages and technologies
  • methods described above and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof.
  • the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware.
  • the means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such permutations and combinations are intended to fall within the scope of the present disclosure.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Navigation (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

Un procédé consiste à récupérer une carte d'une géométrie 3D d'un environnement, la carte comprenant une pluralité de valeurs d'attributs non spatiaux correspondant chacune à l'un d'une pluralité d'attributs non spatiaux et indiquant une pluralité de lectures non spatiales de capteur acquises dans l'environnement, à recevoir une pluralité de lectures de capteur à partir d'un dispositif à l'intérieur de l'environnement, chacune des lectures de capteur correspondant à au moins l'un des attributs non spatiaux, et à mettre en correspondance la pluralité de lectures de capteur reçues avec au moins un emplacement dans la carte pour produire un emplacement de capteur déterminé.
PCT/US2018/042346 2016-03-11 2018-07-16 Alignement de données de signal mesurées avec des données de localisation slam et utilisations associées WO2019018315A1 (fr)

Priority Applications (4)

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EP18834521.9A EP3656138A4 (fr) 2017-07-17 2018-07-16 Alignement de données de signal mesurées avec des données de localisation slam et utilisations associées
US16/745,775 US10989542B2 (en) 2016-03-11 2020-01-17 Aligning measured signal data with slam localization data and uses thereof
US17/202,602 US11506500B2 (en) 2016-03-11 2021-03-16 Aligning measured signal data with SLAM localization data and uses thereof
US17/964,307 US20230288209A1 (en) 2016-03-11 2022-10-12 Aligning measured signal data with slam localization data and uses thereof

Applications Claiming Priority (6)

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US201762533261P 2017-07-17 2017-07-17
US62/533,261 2017-07-17
PCT/US2018/015403 WO2018140701A1 (fr) 2017-01-27 2018-01-26 Dispositif de balayage laser à estimation en temps réel du mouvement propre en ligne
USPCT/US2018/015403 2018-01-26
USPCT/US2018/040269 2018-06-29
PCT/US2018/040269 WO2019006289A1 (fr) 2017-06-30 2018-06-29 Systèmes et procédés d'améliorations de balayage et de mise en correspondance

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PCT/US2018/040269 Continuation-In-Part WO2019006289A1 (fr) 2016-03-11 2018-06-29 Systèmes et procédés d'améliorations de balayage et de mise en correspondance

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WO2021113789A1 (fr) * 2019-12-06 2021-06-10 The Texas A&M University System Cartographie d'objets à l'aide de données de véhicule aérien sans équipage dans des environnements inaccessibles au gps
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