EP3440431A1 - Method for determining the position and orientation of an at least partially automated moving vehicle in an environment by means of landmarks - Google Patents
Method for determining the position and orientation of an at least partially automated moving vehicle in an environment by means of landmarksInfo
- Publication number
- EP3440431A1 EP3440431A1 EP17703416.2A EP17703416A EP3440431A1 EP 3440431 A1 EP3440431 A1 EP 3440431A1 EP 17703416 A EP17703416 A EP 17703416A EP 3440431 A1 EP3440431 A1 EP 3440431A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- landmarks
- vehicle
- pose
- determining
- data
- Prior art date
- Legal status (The legal status 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 status listed.)
- Ceased
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000004807 localization Effects 0.000 claims abstract description 41
- 230000001419 dependent effect Effects 0.000 claims description 4
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 230000008901 benefit Effects 0.000 description 6
- 238000001514 detection method Methods 0.000 description 6
- 238000013459 approach Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 230000009467 reduction Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000011157 data evaluation Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
- G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
-
- 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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30244—Camera pose
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
Definitions
- the invention relates to a method for determining a pose of an at least partially automated moving vehicle in an environment by means of landmarks, wherein the vehicle is moved in the environment and whereby a sequence of localization scenarios is generated, and wherein landmark data for
- Vehicle control system are processed digitally.
- Pose is understood in the field of technology as the spatial position of an object, namely the position and orientation of an object in two-dimensional space or in three-dimensional space.
- the method for determining the pose of the vehicle is based at least additionally on landmarks in the surroundings of the vehicle, wherein a pose base can represent GPS data, for example.
- position data of the vehicle can be enriched based on GPS data with data generated from the recognition of landmarks.
- orientation for example, the direction of travel of the vehicle can be largely determined by landmarks.
- Determining a pose of the vehicle based on landmarks is greater than the accuracy of the determination with GPS data.
- vehicle control system essentially encompasses all of them Components that are necessary to capture the pose, the evaluation of the data and finally the control of the vehicle.
- DE 10 2014 206 901 A1 discloses a method for
- Situation recognition is based, on the one hand, on an environment detection by means of environment sensors, comprising ultrasound, laser, radar, infrared sensors, capacitive sensors, LIDAR sensors and / or video image acquisition.
- environment sensors comprising ultrasound, laser, radar, infrared sensors, capacitive sensors, LIDAR sensors and / or video image acquisition.
- the situation detection is based on the movement of the vehicle in traffic on the detection of objects outside the vehicle, wherein
- Whistleblower are relevant, which indicate a specific situation. These can be, for example, optical markers, objects or
- DE 10 2010 042 063 A1 describes a video-based registration of landmarks, and the landmark data is coupled with GPS or Galileo data for both coarse and more accurate fine positioning of the vehicle.
- the position determination device is to
- Vehicle and additionally or alternatively landmark-based perform.
- vehicle control systems are provided, and in the vehicle control system, the data on the
- Landmarks for determining the pose of the vehicle digitally processed.
- localization yields hereinafter referred to as localization scenario
- a much less accurate localization may be sufficient.
- data is generated for processing in the vehicle control system that is unnecessary for reliably guiding an at least partially automated vehicle.
- data volumes are generated which load the system unnecessarily and excessively bind a data memory of the system with regard to available resources.
- Number of landmarks would allow a significant reduction in the required computing power.
- the object of the invention is the development of a method for determining a pose of at least one partially automated moving vehicle, wherein the method should be designed such that the necessary computing power and the amount of data to be processed is reduced.
- the semi-automated moving vehicle should be able to be guided safely unchanged.
- the invention includes the technical teaching that the amount of
- Landmark data is increased or decreased depending on the localization scenarios as needed.
- the advantage of the invention lies in an overall smaller amount of data for determining a pose of a vehicle driving at least partially automated, wherein the data volume for processing in the
- Vehicle control system is reduced, also a data volume to be transmitted is reduced, which is exchanged, for example, with a back-end server.
- Determination of the pose is generated and that a reduced amount of landmark data is generated by a smaller detail content for the determination of the pose. Also, the method according to the invention results in an increased number of landmarks being used to process a larger number of landmarks for the determination of the pose, and that a smaller number of landmarks are used for a smaller number of landmarks
- Landmarks for the determination of the pose is processed.
- the narrowing of the landmark data can be achieved, for example, by selecting from a set of available landmarks for determining the pose those landmarks that altogether enable, with a minimum number, a sufficient determination of the pose of the vehicle.
- a data memory is provided, in particular as
- environments are provided by means of the data memory, in which information about landmarks are present, wherein the map is divided into areas that represent respective localization scenarios.
- Landmarks may be, for example, traffic signs, traffic lights, lampposts, curbs or lane markings.
- Other landmarks are nearby objects, such as houses, where landmarks are generally selected from immobile objects.
- the map is divided into areas that reflect the scene context, such as the scene context "Crossing without
- the scene context can be retrieved via a coarse, GPS-based global pose through the highly automated vehicle system.
- the method provides that an odometry error model and / or a generic expression is activated, in particular for determining an odometry offset when starting an automated journey with the partially automated vehicle.
- an odometry error model and / or a generic expression is activated, in particular for determining an odometry offset when starting an automated journey with the partially automated vehicle.
- Activation of an odometry error model or a corresponding generic characteristic achieves that the vehicle can be started for the determination of the odometry offset with the start of the highly automated vehicle system, and at a corresponding time the data evaluation takes place added over the landmarks, if appropriate
- the orientation of the vehicle on the basis of landmarks serves to correct the error influence in order to enable highly automated driving of the vehicle, especially in the urban environment.
- Landmarks so that a smaller number of landmarks to be captured and processed is sufficient.
- the localization accuracy is set to an accuracy that can be achieved by the landmarks that can be observed from the stand, taking into account the on-board sensor technology.
- Localization accuracy is based on a statistical method.
- Localization accuracy can be selected, or GPS coordinates are used as a rough position estimate. From the starting point of the trip, error propagation with respect to localization accuracy is developed by adding to the initial uncertainty the errors resulting from the application of the odometry error model. Reached the
- Total error a certain threshold, which depends on the currently driven scenario, then the on-board sensor landmarks of a certain distribution and number searched.
- the on-board sensor landmarks of a certain distribution and number searched In this case, according to the invention, only so many landmarks are searched for, as required by the localization scenario.
- the number and the distribution of landmarks on the localization accuracy to be achieved depends and is selected by the inventive method accordingly.
- the amount of possible landmarks and their vehicle relative position are known from the map loaded from the datastore.
- the detected landmarks are registered with the landmarks from the map using a matching algorithm. Again, the location accuracy is checked with the algorithm mentioned above, and the odometry error is reset.
- those landmarks are selected that are possibly around the vehicle are arranged distributed.
- by the triangulation method is thus by means of an already small number of landmarks one
- the invention is further directed to a vehicle control system for carrying out the method of determining the pose of an at least partially automated vehicle in an environment by landmarks, wherein the vehicle is movable in the environment and wherein a sequence of location scenarios is generated, and wherein the landmark data is for determination the pose of the vehicle by means of the vehicle control system are digitally processable.
- a vehicle control system for carrying out the method of determining the pose of an at least partially automated vehicle in an environment by landmarks, wherein the vehicle is movable in the environment and wherein a sequence of location scenarios is generated, and wherein the landmark data is for determination the pose of the vehicle by means of the vehicle control system are digitally processable.
- Vehicle control system designed so that the length of
- Landmark data is increased or decreased depending on the localization scenarios as needed.
- FIG. 1 shows a localization scenario of a vehicle on a vehicle
- FIG. 2 shows the localization scenario according to FIG. 1, with only two
- Figure 3 is a localization scenario in the traffic with hidden
- Figure 4 is a schematic view of a vehicle with a
- Figures 1 and 2 show an example of a locating scenario for determining a pose of a semi-automated moving vehicle 1 in an environment by landmarks 10.
- the locating scenario A represents the vehicle 1 at a road intersection, and by way of example three
- Landmarks 10 shown of which all landmarks 10 are detected.
- the influence of measurement errors on the localization accuracy is improved by triangulation. If only two landmarks 10 are detected, as shown in FIG. 2, then a pose accuracy 30b which is lower than the localization accuracy 30a according to FIG. 1 in which three landmarks 10 are detected, and the limitation to the pose accuracy 30a is determined Triangulation achieved. This also reduces the risk of obscuring all landmarks 10 by other road users.
- the example of Figures 1 and 2 shows that the influence of measurement errors on the
- Triangulation localization accuracy is improved, and there is a reduced risk of obscuring all landmarks 10 by other road users, for example by trucks on an adjacent lane.
- FIG. 3 shows an example of a vehicle 1 to which another vehicle 1 is accommodating.
- two landmarks 10 are obscured, and by the possible distributed detection of other landmarks 10 may be a
- the number of landmarks 10 per time segment that is to say depending on the localization scenario, can also be used for localization in the case of the highest localization accuracy requirements. In scenarios with low demands on the
- FIG. 4 shows, by way of example, a vehicle 1 with a vehicle control system 100 comprising a data memory 20 and a landmark sensor 40.
- the landmark sensor 40 serves to detect the landmarks 10 and forms, for example, a radar scanner, a LIDAR scanner or the like.
- the invention is not limited in its execution to the above-mentioned preferred embodiment. Rather, a number of variants is conceivable, which makes use of the illustrated solution even with fundamentally different types of use. All from the
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Electromagnetism (AREA)
- Navigation (AREA)
- Traffic Control Systems (AREA)
- Measurement Of Optical Distance (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102016205870.3A DE102016205870A1 (en) | 2016-04-08 | 2016-04-08 | Method for determining a pose of an at least partially automated vehicle in an environment using landmarks |
PCT/EP2017/052486 WO2017174229A1 (en) | 2016-04-08 | 2017-02-06 | Method for determining the position and orientation of an at least partially automated moving vehicle in an environment by means of landmarks |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3440431A1 true EP3440431A1 (en) | 2019-02-13 |
Family
ID=57965951
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP17703416.2A Ceased EP3440431A1 (en) | 2016-04-08 | 2017-02-06 | Method for determining the position and orientation of an at least partially automated moving vehicle in an environment by means of landmarks |
Country Status (6)
Country | Link |
---|---|
US (1) | US10876842B2 (en) |
EP (1) | EP3440431A1 (en) |
JP (1) | JP2019513996A (en) |
CN (1) | CN108885113A (en) |
DE (1) | DE102016205870A1 (en) |
WO (1) | WO2017174229A1 (en) |
Families Citing this family (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11346671B2 (en) * | 2017-09-22 | 2022-05-31 | Continental Automotive Gmbh | Method and system for global localization |
CN108445503B (en) * | 2018-03-12 | 2021-09-14 | 吉林大学 | Unmanned path planning algorithm based on fusion of laser radar and high-precision map |
DE102018208182A1 (en) * | 2018-05-24 | 2019-11-28 | Robert Bosch Gmbh | Method and device for carrying out at least one safety-enhancing measure for a vehicle |
DE102018221178A1 (en) | 2018-12-06 | 2020-06-10 | Robert Bosch Gmbh | Localization system |
DE102019205994A1 (en) * | 2019-04-26 | 2020-10-29 | Robert Bosch Gmbh | Method for forming a localization layer of a digital localization map for automated driving |
DE102019119095B4 (en) * | 2019-07-15 | 2024-06-13 | Man Truck & Bus Se | Method and communication system for supporting at least partially automatic vehicle control |
DE102019119482A1 (en) * | 2019-07-18 | 2021-01-21 | Valeo Schalter Und Sensoren Gmbh | Method for locating an ego vehicle |
FR3100884B1 (en) * | 2019-09-17 | 2021-10-22 | Safran Electronics & Defense | Vehicle positioning method and system implementing an image capture device |
CN115629386B (en) * | 2022-12-21 | 2023-04-11 | 广州森弘信息科技有限公司 | High-precision positioning system and method for automatic parking |
Family Cites Families (18)
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JP3571962B2 (en) | 1999-05-28 | 2004-09-29 | 日本電信電話株式会社 | Position detection method |
US7191056B2 (en) * | 2005-01-04 | 2007-03-13 | The Boeing Company | Precision landmark-aided navigation |
JP4903426B2 (en) * | 2005-11-30 | 2012-03-28 | アイシン・エィ・ダブリュ株式会社 | Image recognition apparatus and method, and vehicle position recognition apparatus and method |
WO2009098154A1 (en) * | 2008-02-04 | 2009-08-13 | Tele Atlas North America Inc. | Method for map matching with sensor detected objects |
JP5116555B2 (en) | 2008-04-25 | 2013-01-09 | 三菱電機株式会社 | LOCATION DEVICE, LOCATION SYSTEM, LOCATION SERVER DEVICE, AND LOCATION METHOD |
DE102008057139A1 (en) * | 2008-04-25 | 2009-11-12 | Siemens Aktiengesellschaft | Computer-based landmark e.g. polygonal framed plane section, selection method for localization of robot, involves selecting partial quantity of landmarks if quality criterion meets preset criterion with respect to high quality of estimation |
NO20082337L (en) * | 2008-05-22 | 2009-11-23 | Modulprodukter As | Method of producing road maps and use of the same, as well as road map system |
DE102010042063B4 (en) | 2010-10-06 | 2021-10-28 | Robert Bosch Gmbh | Method and device for determining processed image data about the surroundings of a vehicle |
US8862395B2 (en) * | 2011-01-31 | 2014-10-14 | Raytheon Company | Coded marker navigation system and method |
EP2490092B1 (en) * | 2011-02-16 | 2013-09-18 | Siemens Aktiengesellschaft | Method for autonomous localisation of a driver-less motorised vehicle |
US9395188B2 (en) | 2011-12-01 | 2016-07-19 | Maxlinear, Inc. | Method and system for location determination and navigation using structural visual information |
US9037411B2 (en) * | 2012-05-11 | 2015-05-19 | Honeywell International Inc. | Systems and methods for landmark selection for navigation |
JP2014066635A (en) * | 2012-09-26 | 2014-04-17 | Toyota Motor Corp | Own vehicle position calibration device and own vehicle position calibration method |
DE102014201824A1 (en) * | 2014-02-03 | 2015-08-06 | Robert Bosch Gmbh | Method and device for determining the position of a vehicle |
DE102014002821A1 (en) * | 2014-02-26 | 2015-08-27 | Audi Ag | Method and system for locating a mobile device |
JP6280409B2 (en) * | 2014-03-25 | 2018-02-14 | 株式会社日立製作所 | Self-vehicle position correction method, landmark data update method, in-vehicle device, server, and self-vehicle position data correction system |
DE102014206901A1 (en) | 2014-04-10 | 2015-10-15 | Robert Bosch Gmbh | User interface for selecting and activating support in maneuver situations |
DE102015011358A1 (en) * | 2015-08-29 | 2016-03-17 | Daimler Ag | Method for operating a vehicle |
-
2016
- 2016-04-08 DE DE102016205870.3A patent/DE102016205870A1/en active Pending
-
2017
- 2017-02-06 CN CN201780022349.7A patent/CN108885113A/en active Pending
- 2017-02-06 WO PCT/EP2017/052486 patent/WO2017174229A1/en active Application Filing
- 2017-02-06 EP EP17703416.2A patent/EP3440431A1/en not_active Ceased
- 2017-02-06 JP JP2018552742A patent/JP2019513996A/en active Pending
- 2017-02-06 US US16/087,506 patent/US10876842B2/en active Active
Also Published As
Publication number | Publication date |
---|---|
CN108885113A (en) | 2018-11-23 |
WO2017174229A1 (en) | 2017-10-12 |
JP2019513996A (en) | 2019-05-30 |
US10876842B2 (en) | 2020-12-29 |
DE102016205870A1 (en) | 2017-10-12 |
US20190101398A1 (en) | 2019-04-04 |
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