CN113748693B - Position and pose correction method and device of roadbed sensor and roadbed sensor - Google Patents
Position and pose correction method and device of roadbed sensor and roadbed sensor Download PDFInfo
- Publication number
- CN113748693B CN113748693B CN202080005468.3A CN202080005468A CN113748693B CN 113748693 B CN113748693 B CN 113748693B CN 202080005468 A CN202080005468 A CN 202080005468A CN 113748693 B CN113748693 B CN 113748693B
- Authority
- CN
- China
- Prior art keywords
- point cloud
- pose
- acquisition device
- current
- point
- 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.)
- Active
Links
Classifications
-
- 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
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
-
- 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
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- Signal Processing (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Length Measuring Devices With Unspecified Measuring Means (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The application discloses a position and posture correction method and device of a roadbed sensor and the roadbed sensor. According to the point cloud of the current frame, determining the current pose of the point cloud acquisition device in a preset point cloud map; when the offset between the current pose and the preset reference pose is larger than an offset threshold, calculating pose adjustment parameters; and the position and posture adjustment parameter-based driving mechanical device adjusts the point cloud acquisition device from the current position and posture to the reference position and posture, so that the position and posture of the roadbed sensor are automatically corrected, and the position and posture correction efficiency and precision of the roadbed sensor are improved.
Description
Technical Field
The application relates to the field of automatic driving, in particular to a position and posture correction method and device of a roadbed sensor and the roadbed sensor.
Background
The roadbed sensor is generally arranged on two sides or above a road, and is provided with a point cloud acquisition device (such as a roadbed sensor, a depth camera and the like), and the roadbed sensor detects road conditions on the road through the point cloud acquisition device. Due to external vibration, bad weather or other factors, the position of the roadbed sensor may deviate, which may cause the roadbed sensor to not normally detect the effective road conditions, thereby affecting the normal use of the roadbed sensor, for example: originally, road conditions of roads can be monitored by 360 degrees through the roadbed sensor, and because the roadbed sensor is inclined under the influence of the outside, the point cloud collecting device can only detect road conditions of partial roads. In the related art, whether an abnormality occurs in a position of a roadbed sensor is generally checked manually, and the manual checking method has the following problems: the efficiency is low, a large amount of time is consumed when a large number of roadbed sensors in a large range are inspected as required, and the labor cost is high; meanwhile, the accuracy of manual investigation is not high, and the condition of missed investigation can occur.
Disclosure of Invention
The technical problem to be solved by the embodiment of the application is to provide a position and posture correction method and device for a roadbed sensor and the roadbed sensor, wherein the position and posture of the roadbed sensor can be automatically adjusted according to the deviation between the current position and the reference position based on the current position and posture of the current frame point cloud generated according to scanning in a point cloud map, and the position and posture adjustment efficiency and accuracy are improved.
In a first aspect, the present application provides a method for correcting a pose of a roadbed sensor, including:
acquiring a current frame point cloud generated by scanning of a point cloud acquisition device;
determining the current pose of the point cloud acquisition device in a preset point cloud map according to the current frame point cloud;
when the offset between the current pose and the preset reference pose is larger than an offset threshold, calculating pose adjustment parameters;
and controlling a mechanical device to adjust the point cloud acquisition device from the current pose to the reference pose based on the pose adjustment parameter.
In one possible design, before the acquiring the current frame point cloud generated by the point cloud acquisition device in a scanning way, the method further includes:
respectively acquiring point clouds in n view fields by the point cloud acquisition devices; wherein, there is a coincidence area between two adjacent fields of view in n fields of view, n is an integer greater than 1;
and splicing the n point clouds based on a point cloud registration algorithm to obtain the point cloud map.
In one possible design, the sum of the horizontal angles of the n fields of view is greater than 360 degrees.
In one possible design, the acquiring the current frame point cloud generated by the point cloud acquisition device includes:
acquiring a first point cloud obtained by scanning a current period of a point cloud acquisition device in a full-range;
and screening in the first point cloud according to a preset distance interval to obtain a current frame point cloud.
In one possible design, the method further comprises:
and when the offset between the current pose and the preset reference pose is larger than an offset threshold, sending pose abnormality prompt information to the user terminal, wherein the pose abnormality information indicates that the pose of the point cloud acquisition device is abnormal.
In one possible design, the method further comprises:
and when the number of point clouds in the point clouds of the current frame is smaller than the preset number, sending shielding prompt information to a user terminal, wherein the shielding prompt information is used for indicating that the point cloud acquisition device is shielded.
In one possible design, the determining, according to the current frame point cloud, a current pose of the point cloud collecting device in a preset point cloud map includes:
determining an intersection ratio region of the point cloud of the current frame and a corresponding intersection ratio region in the point cloud map;
calculating a pose transformation relationship between a parallel-to-cross region in the point cloud of the current frame and a parallel-to-cross region in the point cloud map based on a point cloud registration algorithm;
and calculating the current pose according to the pose transformation relation.
In a second aspect, the present application provides a pose correction device for a roadbed sensor, comprising:
the acquisition unit is used for acquiring the current frame point cloud generated by scanning of the point cloud acquisition device;
the gesture determining unit is used for determining the current gesture of the point cloud collecting device in a preset point cloud map according to the current frame point cloud;
an adjustment amount calculating unit, configured to calculate a pose adjustment parameter when an offset between the current pose and a preset reference pose is greater than an offset threshold;
and the control unit is used for controlling a mechanical device to adjust the point cloud acquisition device from the current pose to the reference pose based on the pose adjustment parameter.
Still another aspect of the present application discloses an attitude correction device of a roadbed sensor, the attitude correction device comprising: a receiver, a transmitter, a memory, and a processor; the processor is used for calling the program codes stored in the memory and executing the pose correction method of the roadbed sensor according to the aspects.
Based on the same application conception, as the principle and beneficial effects of the device for solving the problems can be referred to the method implementation of each possible distance compensation device and the beneficial effects brought by the method implementation, the implementation of the device can be referred to the implementation of the method, and the repetition is omitted.
Yet another aspect of the application provides a computer-readable storage medium having instructions stored therein that, when executed on a computer, cause the computer to perform the method of the above aspects.
Yet another aspect of the application provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of the above aspects.
In the embodiment of the application, the current pose of the point cloud acquisition device in a preset point cloud map is determined according to the point cloud of the current frame; when the offset between the current pose and the preset reference pose is larger than an offset threshold, calculating pose adjustment parameters; the position and posture adjustment parameter-based driving mechanical device adjusts the current position and posture of the point cloud acquisition device to the reference position and posture, so that the position and posture of the roadbed sensor can be automatically corrected, and the problems of low efficiency and inaccuracy caused by manual position and posture adjustment are solved.
Drawings
In order to more clearly describe the embodiments of the present application or the technical solutions in the background art, the following description will describe the drawings that are required to be used in the embodiments of the present application or the background art.
FIG. 1 is a schematic deployment diagram of a roadbed sensor provided by an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for correcting the pose of a roadbed sensor according to an embodiment of the present application;
FIG. 3 is a schematic diagram of the field of view provided by the present embodiment;
fig. 4 and fig. 5 are schematic diagrams of point cloud stitching provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a pose-based correction device according to an embodiment of the present application;
fig. 7 is another schematic structural diagram of a pose-based correction device according to an embodiment of the present application.
Detailed Description
In order to make the objects, features and advantages of the embodiments of the present application more obvious and understandable, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application as detailed in the accompanying claims.
In the description of the present application, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Referring to fig. 1, a deployment schematic diagram of roadbed sensors provided in an embodiment of the present application is shown, where roadbed sensors 101 to 103 are deployed on both sides of a road. The roadbed sensor comprises a pose correction device, a point cloud acquisition device and a mechanical device, wherein the point cloud acquisition device can detect road conditions on a road, such as: the point cloud acquisition device is used for periodically transmitting detection signals, generating echo signals after the detection signals meet vehicles 104 on the road, and generating point clouds according to the echo signals; the point cloud acquisition device can be a laser radar or a depth camera and the like. The mechanical device can be a device for moving in six degrees of freedom, the mechanical device can be a mechanical arm or a six-degree-of-freedom platform, and the mechanical device can drive the point cloud acquisition device to translate along the directions of an x axis, a y axis and a z axis and rotate around the directions of the x axis, the y axis and the z axis. The pose correction device is used for performing subsequent processing on the point cloud acquired by the point cloud acquisition device, for example: and determining the pose, calculating the pose adjustment amount and the like.
Referring to fig. 2, fig. 2 is a pose correction method according to an embodiment of the present application, including but not limited to the following steps:
s201, acquiring a current frame point cloud generated by scanning of a point cloud acquisition device.
The point cloud acquisition device can periodically scan, a frame of point cloud is generated after each scan, the pose correction device acquires the current frame of point cloud generated by the point cloud acquisition device during the current period scan, and the point cloud can be a 3D point cloud, namely the point cloud comprises three-dimensional space coordinates (a coordinate system based on the point cloud acquisition device) and echo intensity. The number and density of point clouds is related to the performance of the point cloud acquisition device, for example: the more the number of lines of the point cloud acquisition device is, the greater the density of the point cloud is; the larger the field of view of the point cloud acquisition device, the greater the number of point clouds.
In one or more embodiments, since points that are too close may fall on the mechanical device, points that are too far are already very sparse, and these two types of points have little effect on pose recognition, in order to reduce the computation load, the embodiment filters the two types of points, and the specific method includes: the pose correction device is provided with a distance interval in advance, after the point cloud acquisition device scans once in a full-range to obtain first point cloud, the first point cloud is screened according to the distance interval, the distances corresponding to the screened point cloud are all in the distance interval, and the screened point cloud is used as the point cloud of the current frame.
For example: the point cloud acquisition device is a laser radar, the full range of the laser radar is 0-20 m, and the laser radar scans a first point cloud corresponding to the full range in the current period. The distance interval preset by the laser radar is 1-5 m, the laser radar traverses the distances corresponding to all points in the first point cloud, and the point with the distance of 1-5 m in the distance interval is taken as the point cloud of the current frame.
In one or more embodiments, the lens of the point cloud collecting device may be blocked by a foreign object, so that the detection effect of the point cloud collecting device may be affected, and the situation in the field of view cannot be accurately reflected. The preset number is related to the line number, the field size and the scanning frequency of the point cloud acquisition device. For example: when the preset number is 10000 and the number of the point clouds of the current frame acquired by the point cloud acquisition device in the current period is less than 10000, shielding prompt information is sent to the mobile terminal which is bound in advance by the user, and the type of the shielding prompt information can be short message, instant communication message, email or multimedia message, etc., and the embodiment of the application is not limited.
S202, determining the current pose of the point cloud acquisition device in a preset point cloud map according to the point cloud of the current frame.
The pose correction device is pre-stored or pre-configured with a point cloud map, and the point cloud map is obtained by splicing multiple frames of point clouds acquired by the point cloud acquisition device under a reference pose, for example: after the roadbed sensor is installed, maintenance personnel test the pose of the point cloud acquisition device to reach a reference pose, then the point cloud acquisition device acquires n point clouds under n view fields, the view fields represent the scanning range of the point cloud acquisition device, and the range of the view fields is determined by a horizontal angle and a vertical angle, for example: the horizontal angle is-30 degrees to +30 degrees, and the vertical angle is-15 degrees to +15 degrees; and (3) overlapping two adjacent fields of view in the n fields of view, namely overlapping the point clouds acquired by the two adjacent fields of view, wherein one field of view corresponds to one point cloud, and splicing the n point clouds according to a point cloud registration algorithm to obtain a complete point cloud map. Further, the sum of the horizontal angles of the n fields of view is greater than 360 degrees, that is, the point cloud acquisition device can scan in the horizontal direction of 360 degrees.
Referring to fig. 3, the lidar collects point clouds in 6 fields of view, and respectively obtains a point cloud 1, a point cloud 2, a point cloud 3, a point cloud 4, a point cloud 5, and a point cloud 6, where an overlapping area exists between two adjacent point clouds in the 6 point clouds, for example, an overlapping area exists between the point cloud 2 and the point cloud 3, a point a is one point in the overlapping area, and the point clouds 1 to 6 are spliced to obtain a static point cloud map.
In one or more embodiments, a point cloud map is obtained by stitching a plurality of point clouds based on a point cloud registration algorithm, which may be an ICP (Iterative Closest Point ) algorithm or NDT (Normal Distributions Transform, normal distribution transform) algorithm.
For example: referring to fig. 4 and 5, the splicing process of the point cloud a and the point cloud B is described: the condition that the point cloud a and the point cloud B can be spliced is that an IOU (interaction-over-Union) exists in the two, the IOU can also be used for jointing the areas or the common areas, and the IOU area is a clue that the point cloud a and the point cloud B are spliced. The point cloud A and the point cloud B are 3D point clouds, so that the point cloud A and the point cloud B have outline characteristics, the point cloud A is fixed, the point cloud B continuously adjusts the pose (position and orientation) of the point cloud A, and when the IOU in the point cloud A and the IOU in the point cloud B are in the same pose (as shown in the situation of fig. 5), the point cloud A and the point cloud B are successfully spliced, and the spliced point cloud is shown in fig. 5.
In this embodiment, the method for determining the current pose of the point cloud acquisition frame in the point cloud map according to the current frame point cloud includes: determining an intersection ratio region of the point cloud of the current frame and a corresponding intersection ratio region in the point cloud map; calculating a pose transformation relationship between a parallel-to-cross region in the point cloud of the current frame and a parallel-to-cross region in the point cloud map based on a point cloud registration algorithm; and calculating the current pose according to the pose transformation relation. The point cloud registration algorithm may be an ICP algorithm or an NDT algorithm.
In this embodiment, the process of determining the current pose by using the ICP algorithm includes:
1) And searching a corresponding nearby point set on the point cloud map according to the point coordinates in the point cloud of the current frame.
2) And calculating barycentric position coordinates of two point sets (a point cloud of the current frame and a point cloud map), and carrying out point set centralization to generate a new point set.
3) And calculating a positive definite matrix N from the new point set, and calculating the maximum eigenvalue and the maximum eigenvector of the N.
4) Since the maximum eigenvector is equivalent to the rotation quaternion when the sum of squares of the residuals is minimum, the quaternion is converted into a rotation matrix R.
5) After the rotation matrix R is determined, the barycentric difference of only two point sets from the translation vector t can be determined by barycentric points in two coordinate systems and the rotation matrix.
6) And calculating the rotated point cloud P' lk by the current frame point cloud Plk. And calculating a distance square sum value fk+1 through the point cloud Plk and the point cloud P' lk of the current frame, and taking the absolute value of the difference between the two continuous distance square sums as an iteration judgment value.
7) And stopping iteration when the iteration judgment value is larger than a threshold value, otherwise repeating the steps 1 to 6 until the condition is met.
In this embodiment, the process of determining the current pose by using the NDT algorithm includes:
1) Dividing the space occupied by the point cloud map into grids or voxels (pixels) of a specified size (CellSize); and calculates the multidimensional normal distribution parameter of each grid.
2) The transformation parameter p is initialized (either with a value of zero or with a mile counter data assignment).
3) For the current frame point cloud to be registered, it is converted into a grid of the point cloud map by transformation T.
4) And calculating the probability density of each conversion point according to the normal distribution parameters.
5) An NDT registration score is calculated, which is obtained by adding the probability densities calculated for each grid.
6) The objective function score-score is optimized according to the newton optimization algorithm, i.e. the transformation parameter p is found such that the score value is maximized.
7) Jump to step 3 and continue until convergence conditions are reached.
For example: in the point cloud registration process, referring to the process of constructing the point cloud map in fig. 4 and fig. 5, there is an IOU between the point cloud of the current frame and the precision map, and the pose of the point cloud of the current frame is continuously adjusted to enable the IOU of the point cloud of the current frame to coincide with the IOU in the point cloud map, so that the calculated pose is the pose of the point cloud of the current frame, the pose can be represented by parameters of 6 dimensions (x, y, z, α, β, γ), the coordinate system of the pose can be the coordinate system of the roadbed sensor, and the (x, y, z) is the degree of freedom of movement along the directions of three rectangular coordinate axes of x, y and z, and the (α, β, γ) is the degree of freedom of rotation around the three coordinate axes.
And S203, calculating pose adjustment parameters when the offset between the current pose and the preset reference pose is larger than an offset threshold.
The offset threshold comprises a translation offset threshold and/or an angle offset threshold, and the reference pose is a pose of the point cloud acquisition device when the point cloud acquisition device generates a point cloud map. The pose mechanical device can calculate pose adjustment parameters between the current pose and a preset reference pose according to a space geometric relationship, wherein the pose adjustment parameters comprise rotation amounts (rx, ry, rz) and translation amounts (dx, dy, dz), the rotation amounts represent angles of rotation around an x axis, a y axis or a z axis, and the translation amounts represent translation distances along the x axis, the y axis or the z axis so as to adjust the current pose of the roadbed sensor to the reference pose.
In one or more embodiments, the method further comprises:
and when the offset between the current pose and the preset reference pose is larger than an offset threshold, sending pose abnormality prompt information to the user terminal, wherein the pose abnormality information indicates that the pose of the point cloud acquisition device is abnormal. The pose abnormal prompt information can be short message, instant communication message, email or multimedia message, etc., and the embodiment of the application is not limited.
S204, controlling a mechanical device to adjust the point cloud acquisition device from the current pose to the reference pose based on the pose adjustment parameters.
The pose correction device sends a control signal to the mechanical device, the control signal indicates the mechanical device to translate and rotate according to the pose adjustment parameters calculated in the step S204, the mechanical device can be a six-degree-of-freedom mechanical arm or platform and the like, and the mechanical device drives the point cloud acquisition device to adjust the pose according to the pose adjustment parameters calculated in the step S204, so that the point cloud acquisition device is adjusted to be a reference pose from the current pose.
According to the description of fig. 2, determining the current pose of the point cloud acquisition device in a preset point cloud map according to the point cloud of the current frame; when the offset between the current pose and the preset reference pose is larger than an offset threshold, calculating pose adjustment parameters; the position and posture adjustment parameter-based driving mechanical device adjusts the current position and posture of the point cloud acquisition device to the reference position and posture, so that the position and posture of the roadbed sensor can be automatically corrected, and the problems of low efficiency and inaccuracy caused by manual position and posture adjustment are solved.
The above details a method for correcting the pose of the roadbed sensor according to the embodiment of the present application, and the pose correction device (hereinafter referred to as device 3) of the roadbed sensor according to the embodiment of the present application is provided below.
The apparatus 3 shown in fig. 6 may implement the pose correction method of the roadbed sensor of the embodiment shown in fig. 2, the apparatus 3 including an acquisition unit 301, a pose determination unit 302, an adjustment amount calculation unit 303, and a control unit 304.
An obtaining unit 301, configured to obtain a current frame point cloud generated by scanning by a point cloud collecting device;
a pose determining unit 302, configured to determine, according to the current frame point cloud, a current pose of the point cloud collecting device in a preset point cloud map;
an adjustment amount calculating unit 303, configured to calculate a pose adjustment parameter when an offset amount between the current pose and a preset reference pose is greater than an offset amount threshold;
and the control unit 304 is used for controlling a mechanical device to adjust the point cloud acquisition device from the current pose to the reference pose based on the pose adjustment parameter.
In one or more embodiments, the apparatus 3 further comprises:
the map generation unit is used for respectively acquiring point clouds in n view fields through the point cloud acquisition device; wherein, there is a coincidence area between two adjacent fields of view in n fields of view, n is an integer greater than 1;
and splicing the n point clouds based on a point cloud registration algorithm to obtain the point cloud map.
In one or more embodiments, the sum of the horizontal angles of the n fields of view is greater than 360 degrees.
In one or more embodiments, the acquiring unit 301 is specifically configured to:
acquiring a first point cloud obtained by scanning a current period of a point cloud acquisition device in a full-range;
and screening in the first point cloud according to a preset distance interval to obtain a current frame point cloud.
In one or more embodiments, the apparatus 3 further comprises:
the prompting unit is used for sending pose abnormal prompting information to the user terminal when the offset between the current pose and the preset reference pose is larger than the offset threshold, wherein the pose abnormal information indicates that the pose of the point cloud acquisition device is abnormal.
In one or more embodiments, the hint unit is further configured to: and when the number of point clouds in the point clouds of the current frame is smaller than the preset number, sending shielding prompt information to a user terminal, wherein the shielding prompt information is used for indicating that the point cloud acquisition device is shielded.
In one or more embodiments, the pose determination unit 302 is specifically configured to:
determining an intersection ratio region of the point cloud of the current frame and a corresponding intersection ratio region in the point cloud map;
calculating a pose transformation relationship between a parallel-to-cross region in the point cloud of the current frame and a parallel-to-cross region in the point cloud map based on a point cloud registration algorithm;
and calculating the current pose according to the pose transformation relation.
The embodiments of the present application and the embodiments of the methods of fig. 1 to 5 are based on the same concept, and the technical effects brought by the embodiments are the same, and the specific process can refer to the description of the embodiments of the methods of fig. 1 to 5, which is not repeated here.
The device 3 may be a field-programmable gate array (FPGA) for implementing relevant functions, an application specific integrated chip, a system on chip (SoC), a central processing unit (central processor unit, CPU), a network processor (network processor, NP), a digital signal processing circuit, a micro-pose correction device (micro controller unit, MCU), a programmable logic device (programmable logic device, PLD) or other integrated chips.
The above details a method for correcting the pose of the roadbed sensor according to the embodiment of the present application, and a pose correction device (hereinafter referred to as device 4) according to the embodiment of the present application is provided below.
Fig. 7 is a schematic structural diagram of a device provided in an embodiment of the present application, hereinafter referred to as device 4, where the device 4 may be integrated with the roadbed sensor according to the above embodiment, as shown in fig. 4, and the device includes: memory 402, processor 401, transmitter 404, and receiver 403.
The memory 402 may be a separate physical unit, and may be connected to the processor 401, the transmitter 404, and the receiver 403 via buses. The memory 402, the processor 401, the transmitter 404, and the receiver 401 may be integrated together, implemented by hardware, or the like.
The transmitter 404 is for transmitting signals and the receiver 403 is for receiving signals.
The memory 402 is used for storing a program implementing the above method embodiment, or each module of the apparatus embodiment, and the processor 401 calls the program to perform the operations of the above method embodiment.
Alternatively, when part or all of the pose correction method of the roadbed sensor of the above-described embodiment is implemented by software, the apparatus may include only the processor. The memory for storing the program is located outside the device and the processor is connected to the memory via a circuit/wire for reading and executing the program stored in the memory.
The processor may be a central processor (central processing unit, CPU), a network processor (network processor, NP) or a combination of CPU and NP.
The processor may further comprise a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (programmable logic device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (complex programmable logic device, CPLD), a field-programmable gate array (field-programmable gate array, FPGA), general-purpose array logic (generic array logic, GAL), or any combination thereof.
The memory may include volatile memory (RAM), such as random-access memory (RAM); the memory may also include a nonvolatile memory (non-volatile memory), such as a flash memory (flash memory), a hard disk (HDD) or a Solid State Drive (SSD); the memory may also comprise a combination of the above types of memories.
In the above embodiments, the transmitting unit or the transmitter performs the steps of transmitting the above embodiments of the method, the receiving unit or the receiver performs the steps of receiving the above embodiments of the method, and other steps are performed by other units or processors. The transmitting unit and the receiving unit may constitute a transceiving unit, and the receiver and the transmitter may constitute a transceiver.
The embodiment of the application also provides a computer storage medium which stores a computer program for executing the position and posture correcting method of the roadbed sensor provided by the embodiment.
The embodiment of the application also provides a computer program product containing instructions, which when run on a computer, cause the computer to execute the position correcting method of the roadbed sensor provided by the embodiment.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (8)
1. The utility model provides a position appearance correction method of road bed sensor which characterized in that includes:
respectively acquiring point clouds in n view fields by a point cloud acquisition device; wherein, there is a coincidence area between two adjacent fields of view in said n fields of view, n is the integer greater than 1; the sum of the horizontal angles of the n fields of view is greater than 360 degrees;
splicing n point clouds based on a point cloud registration algorithm to obtain a point cloud map;
acquiring a current frame point cloud generated by scanning of the point cloud acquisition device;
determining an intersection ratio region of the point cloud of the current frame and a corresponding intersection ratio region in the point cloud map;
calculating a pose transformation relationship between a parallel-to-cross region in the point cloud of the current frame and a parallel-to-cross region in the point cloud map based on a point cloud registration algorithm; the point cloud registration algorithm is an iterative closest point algorithm or a normal distribution conversion algorithm;
calculating the current pose according to the pose transformation relation; wherein the current pose is represented by parameters having six degrees of freedom (x, y, z, α, β, γ); of the six degrees of freedom, (x, y, z) is a degree of freedom of movement in the coordinate system of the roadbed sensor along the directions of three coordinate axes of x, y and z, and (α, β, γ) is a degree of freedom of rotation about the three coordinate axes;
calculating pose adjustment parameters when the offset between the current pose and a preset reference pose is larger than an offset threshold;
and controlling a mechanical device to adjust the point cloud acquisition device from the current pose to the reference pose based on the pose adjustment parameters, wherein the mechanical device is a mechanical arm or a platform with the six degrees of freedom.
2. The method of claim 1, wherein the acquiring the current frame point cloud generated by the point cloud acquisition device comprises:
acquiring a first point cloud obtained by scanning a current period of a point cloud acquisition device in a full-range;
and screening in the first point cloud according to a preset distance interval to obtain a current frame point cloud.
3. The method as recited in claim 1, further comprising:
and when the offset between the current pose and the preset reference pose is larger than an offset threshold, sending pose abnormality prompt information to the user terminal, wherein the pose abnormality information indicates that the pose of the point cloud acquisition device is abnormal.
4. The method as recited in claim 1, further comprising:
and when the number of point clouds in the point clouds of the current frame is smaller than the preset number, sending shielding prompt information to a user terminal, wherein the shielding prompt information is used for indicating that the point cloud acquisition device is shielded.
5. The utility model provides a position appearance correcting unit of road bed sensor which characterized in that includes:
the map generation unit is used for respectively acquiring point clouds in n view fields through the point cloud acquisition device; wherein, there is a coincidence area between two adjacent fields of view in said n fields of view, n is the integer greater than 1; the sum of the horizontal angles of the n fields of view is greater than 360 degrees; splicing n point clouds based on a point cloud registration algorithm to obtain a point cloud map;
the acquisition unit is used for acquiring the current frame point cloud generated by scanning of the point cloud acquisition device;
the pose determining unit is used for determining an intersection ratio region of the point cloud of the current frame and a corresponding intersection ratio region in the point cloud map; calculating a pose transformation relationship between a parallel-to-cross region in the point cloud of the current frame and a parallel-to-cross region in the point cloud map based on a point cloud registration algorithm; the point cloud registration algorithm is an iterative closest point algorithm or a normal distribution conversion algorithm; calculating the current pose according to the pose transformation relation;
the current pose is represented by parameters having six degrees of freedom (x, y, z, α, β, γ); of the six degrees of freedom, (x, y, z) is a degree of freedom of movement in the coordinate system of the roadbed sensor along the directions of three coordinate axes of x, y and z, and (α, β, γ) is a degree of freedom of rotation about the three coordinate axes;
an adjustment amount calculating unit, configured to calculate a pose adjustment parameter when an offset between the current pose and a preset reference pose is greater than an offset threshold;
and the control unit is used for controlling a mechanical device to adjust the point cloud acquisition device from the current pose to the reference pose based on the pose adjustment parameter, wherein the mechanical device is a mechanical arm or a platform with the six degrees of freedom.
6. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method steps of any of claims 1-4.
7. A position and orientation correction device for a roadbed sensor, characterized by comprising a processor and a memory, the memory being for storing a computer program or instructions, the processor being for executing the computer program or instructions in the memory to implement the method according to any one of claims 1 to 4.
8. A roadbed sensor, comprising: the pose correction device, point cloud acquisition device and mechanical device of claim 5 or 7; the mechanical device is used for bearing the point cloud acquisition device.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2020/081848 WO2021189479A1 (en) | 2020-03-27 | 2020-03-27 | Pose correction method and device for roadbed sensor, and roadbed sensor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113748693A CN113748693A (en) | 2021-12-03 |
| CN113748693B true CN113748693B (en) | 2023-09-15 |
Family
ID=77889897
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202080005468.3A Active CN113748693B (en) | 2020-03-27 | 2020-03-27 | Position and pose correction method and device of roadbed sensor and roadbed sensor |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113748693B (en) |
| WO (1) | WO2021189479A1 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115235525B (en) * | 2021-12-07 | 2023-05-23 | 上海仙途智能科技有限公司 | Sensor detection method, sensor detection device, electronic equipment and readable storage medium |
| CN116413702A (en) * | 2021-12-30 | 2023-07-11 | 上海禾赛科技有限公司 | Laser radar pose diagnosis method, laser radar and automatic driving vehicle |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104091321A (en) * | 2014-04-14 | 2014-10-08 | 北京师范大学 | Multi-level-point-set characteristic extraction method applicable to ground laser radar point cloud classification |
| CN105976353A (en) * | 2016-04-14 | 2016-09-28 | 南京理工大学 | Spatial non-cooperative target pose estimation method based on model and point cloud global matching |
| CN107564062A (en) * | 2017-08-16 | 2018-01-09 | 清华大学 | Pose method for detecting abnormality and device |
| CN108932736A (en) * | 2018-05-30 | 2018-12-04 | 南昌大学 | Two-dimensional laser radar Processing Method of Point-clouds and dynamic robot pose calibration method |
| CN109544630A (en) * | 2018-11-30 | 2019-03-29 | 南京人工智能高等研究院有限公司 | Posture information determines method and apparatus, vision point cloud construction method and device |
| CN109633665A (en) * | 2018-12-17 | 2019-04-16 | 北京主线科技有限公司 | The sparse laser point cloud joining method of traffic scene |
| CN110084832A (en) * | 2019-04-25 | 2019-08-02 | 亮风台（上海）信息科技有限公司 | Correcting method, device, system, equipment and the storage medium of camera pose |
| CN110658530A (en) * | 2019-08-01 | 2020-01-07 | 北京联合大学 | Map construction method and system based on double-laser-radar data fusion and map |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201116961D0 (en) * | 2011-09-30 | 2011-11-16 | Bae Systems Plc | Fast calibration for lidars |
| US10127434B2 (en) * | 2016-07-15 | 2018-11-13 | Tyco Fire & Security Gmbh | Techniques for built environment representations |
| CN107167788B (en) * | 2017-03-21 | 2020-01-21 | 深圳市速腾聚创科技有限公司 | Method and system for obtaining laser radar calibration parameters and laser radar calibration |
| US11475351B2 (en) * | 2017-11-15 | 2022-10-18 | Uatc, Llc | Systems and methods for object detection, tracking, and motion prediction |
| CN110084116B (en) * | 2019-03-22 | 2022-02-01 | 深圳市速腾聚创科技有限公司 | Road surface detection method, road surface detection device, computer equipment and storage medium |
| CN110260867B (en) * | 2019-07-29 | 2021-06-15 | 浙江华睿科技有限公司 | Method, equipment and device for determining and correcting neutral position in robot navigation |
| CN110879400A (en) * | 2019-11-27 | 2020-03-13 | 炬星科技（深圳）有限公司 | Method, equipment and storage medium for fusion positioning of laser radar and IMU |
-
2020
- 2020-03-27 CN CN202080005468.3A patent/CN113748693B/en active Active
- 2020-03-27 WO PCT/CN2020/081848 patent/WO2021189479A1/en active Application Filing
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104091321A (en) * | 2014-04-14 | 2014-10-08 | 北京师范大学 | Multi-level-point-set characteristic extraction method applicable to ground laser radar point cloud classification |
| CN105976353A (en) * | 2016-04-14 | 2016-09-28 | 南京理工大学 | Spatial non-cooperative target pose estimation method based on model and point cloud global matching |
| CN107564062A (en) * | 2017-08-16 | 2018-01-09 | 清华大学 | Pose method for detecting abnormality and device |
| CN108932736A (en) * | 2018-05-30 | 2018-12-04 | 南昌大学 | Two-dimensional laser radar Processing Method of Point-clouds and dynamic robot pose calibration method |
| CN109544630A (en) * | 2018-11-30 | 2019-03-29 | 南京人工智能高等研究院有限公司 | Posture information determines method and apparatus, vision point cloud construction method and device |
| CN109633665A (en) * | 2018-12-17 | 2019-04-16 | 北京主线科技有限公司 | The sparse laser point cloud joining method of traffic scene |
| CN110084832A (en) * | 2019-04-25 | 2019-08-02 | 亮风台（上海）信息科技有限公司 | Correcting method, device, system, equipment and the storage medium of camera pose |
| CN110658530A (en) * | 2019-08-01 | 2020-01-07 | 北京联合大学 | Map construction method and system based on double-laser-radar data fusion and map |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021189479A1 (en) | 2021-09-30 |
| CN113748693A (en) | 2021-12-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113748357B (en) | Attitude correction method, device and system of laser radar | |
| CN107340522B (en) | Laser radar positioning method, device and system | |
| CN105136054B (en) | The fine deformation monitoring method of structures and system based on Three Dimensional Ground laser scanning | |
| CN113748693B (en) | Position and pose correction method and device of roadbed sensor and roadbed sensor | |
| CN110632582B (en) | Sound source positioning method, device and storage medium | |
| CN111563450B (en) | Data processing method, device, equipment and storage medium | |
| CN112270719B (en) | Camera calibration method, device and system | |
| CN111080662A (en) | Lane line extraction method and device and computer equipment | |
| JP2022515225A (en) | Sensor calibration methods and equipment, storage media, calibration systems and program products | |
| CN112106111A (en) | Calibration method, calibration equipment, movable platform and storage medium | |
| CN104517318A (en) | System and method for three-dimensional measurement simulation point selection | |
| CN112381026A (en) | Method, system and device for detecting channel shoreline and storage medium | |
| CN114814758B (en) | Camera-millimeter wave radar-laser radar combined calibration method and device | |
| CN112513679A (en) | Target identification method and device | |
| CN111427019A (en) | Roadside radar self-adaptive correction method and system | |
| CN112146848A (en) | Method and device for determining distortion parameter of camera | |
| JP2017524122A (en) | Method and apparatus for measuring displacement of mobile platform | |
| CN111142514A (en) | Robot and obstacle avoidance method and device thereof | |
| CN116524109B (en) | WebGL-based three-dimensional bridge visualization method and related equipment | |
| CN112669458A (en) | Method, device and program carrier for ground filtering based on laser point cloud | |
| CN118230231A (en) | Pose construction method and device of unmanned vehicle, electronic equipment and storage medium | |
| CN116128886A (en) | Point cloud data segmentation method and device, electronic equipment and storage medium | |
| CN113513988B (en) | Laser radar target detection method and device, vehicle and storage medium | |
| CN113203424B (en) | Multi-sensor data fusion method and device and related equipment | |
| CN117387572A (en) | Levelness checking method, terminal and computer readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |