CN114152935A - Method, device and equipment for evaluating radar external parameter calibration precision - Google Patents
Method, device and equipment for evaluating radar external parameter calibration precision Download PDFInfo
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Abstract
The application discloses a method, a device and equipment for evaluating radar external reference calibration precision. The evaluation method comprises the following steps: acquiring a registration coordinate system, first point cloud data and second point cloud data of the M radars based on point cloud registration, wherein the first point cloud data are point cloud data of N calibration plates in each radar field of view in the registration coordinate system, and the second point cloud data are point cloud data of the N calibration plates in a reference map coordinate system; calculating an angle error and a displacement error of each radar from a registration coordinate system to a reference map coordinate system according to the first point cloud data and the second point cloud data; and calculating external reference calibration errors among the radars at least according to the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system. In the method, the external reference calibration error among the multiple radars is calculated to guide an operator to carry out quantitative judgment, and the automation efficiency is greatly improved.
Description
Technical Field
The application relates to the technical field of laser radars, in particular to a method, a device and equipment for evaluating radar external reference calibration precision.
Background
Lidar is a target detection technology. The laser is used as a signal light source, and the laser is emitted to a target object, so that a reflection signal of the target object is collected, and information such as the direction and the speed of the target object is obtained. The laser radar has the advantages of high measurement precision, strong anti-interference capability and the like, and is widely applied to the fields of remote sensing, measurement, intelligent driving, robots and the like.
Currently, to achieve large field of view, even full field coverage, a multi-radar configuration may be employed. However, due to different installation positions, coordinate systems of multiple radars are not uniform, and external parameters of the multiple radars need to be calibrated.
However, how to evaluate the accuracy of the multi-radar external reference calibration is an urgent problem to be solved.
Disclosure of Invention
The application provides an evaluation method, device and equipment for radar external reference calibration precision, which are used for providing calibration precision while completing external reference calibration, guiding an operator to carry out quantitative judgment and greatly improving automation efficiency.
In a first aspect, the present application provides a method for evaluating radar external reference calibration accuracy, including: acquiring a registration coordinate system, first point cloud data and second point cloud data of M radars based on point cloud registration, wherein the registration coordinate system is a reference map coordinate system obtained based on radar external reference calibration, the first point cloud data are point cloud data of N calibration plates in each radar field in the registration coordinate system, the second point cloud data are point cloud data of the N calibration plates in the reference map coordinate system, M is a positive integer, and N is an integer greater than or equal to 3; calculating an angle error and a displacement error of each radar from a registration coordinate system to a reference map coordinate system according to the first point cloud data and the second point cloud data; and calculating external reference calibration errors among the radars at least according to the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system.
In some possible embodiments, obtaining the first point cloud data and the second point cloud data for the M radars comprises: extracting first point cloud data of the N calibration plates in a registration coordinate system and extracting second point cloud data of the N calibration plates in a point cloud overlapping area of a reference map coordinate system by adopting a point cloud extraction algorithm; and the point cloud overlapping area is an overlapping area of point cloud data in the reference map coordinate system and point cloud data in the registration coordinate system.
In some possible embodiments, calculating an angle error of each radar from the reference coordinate system to the reference map coordinate system according to the first point cloud data and the second point cloud data includes: calculating first normal vectors of the N calibration plates in the registration coordinate system according to the first point cloud data of each radar; calculating N second normal vectors in a reference map coordinate system from the second point cloud data of each radar; and calculating the angle error of each radar from the registration coordinate system to the reference map coordinate system according to the first normal vector and the second normal vector.
In some possible embodiments, calculating an angle error of each radar from the reference coordinate system to the reference map coordinate system according to the first normal vector and the second normal vector includes: and calculating a deviation matrix of a rotation matrix of each radar from the radar coordinate system to the registration coordinate system and a rotation matrix of each radar from the radar coordinate system to the reference map coordinate system by using a least square method according to the first normal vector and the second normal vector, wherein the deviation matrix is used for representing the angle error of each radar from the registration coordinate system to the reference map coordinate system.
In some possible embodiments, calculating a displacement error of each radar from the registration coordinate system to the reference map coordinate system according to the first point cloud data and the second point cloud data includes: according to the first point cloud data, obtaining first intersection point data of S calibration plates intersected in the N calibration plates, wherein S is an integer which is greater than or equal to 3 and less than or equal to N; according to the second point cloud data, second intersection data of the S calibration plates in the reference map coordinate system are obtained; and calculating the displacement error of each radar from the registration coordinate system to the reference map coordinate system according to the first intersection data and the second intersection data.
In some possible embodiments, obtaining external reference calibration errors of the M radars according to an angle error and a displacement error of each radar from the registration coordinate system to the reference map coordinate system includes: calculating a transformation matrix of each radar from the registration coordinate system to the reference map coordinate system according to the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system; determining an mth radar in the M radars as a reference radar, wherein M is a positive integer less than or equal to M; and calculating the angle error and the displacement error of each radar relative to the reference radar at least according to the transformation matrix of each radar from the registration coordinate system to the reference map coordinate system and the transformation matrix of the reference radar from the registration coordinate system to the reference map coordinate system.
In some possible embodiments, calculating the angular error and the displacement error of each radar with respect to the reference radar based on at least a transformation matrix of each radar from the registration coordinate system to the reference map coordinate system and a transformation matrix of the reference radar from the registration coordinate system to the reference map coordinate system includes: and calculating the angle error and the displacement error of each radar relative to the reference radar according to the transformation matrix of each radar from the registration coordinate system to the reference map coordinate system, the transformation matrix of the reference radar from the registration coordinate system to the reference map coordinate system and the transformation matrix of each radar coordinate system to the registration coordinate system.
In a second aspect, the present application provides an evaluation apparatus for radar external reference calibration precision, where the evaluation apparatus may be an external reference calibration device of a laser radar, or a chip or a system on a chip in the external reference calibration device, or may also be a functional module in the external reference calibration device of the laser radar, which is used to implement the method described in each of the above embodiments. The calibration device can implement the functions executed by the calibration device in the above embodiments, and these functions can be implemented by hardware executing corresponding software. These hardware or software include one or more functionally corresponding modules. The evaluation device includes: the external reference calibration module is used for obtaining a registration coordinate system, first point cloud data and second point cloud data of M radars based on point cloud registration, wherein the registration coordinate system is a reference map coordinate system obtained based on radar external reference calibration, the first point cloud data are point cloud data of N calibration plates in each radar field in the registration coordinate system, the second point cloud data are point cloud data of the N calibration plates in the reference map coordinate system, M is a positive integer, and N is an integer greater than or equal to 3; the error calculation module is used for calculating the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system according to the first point cloud data and the second point cloud data; and calculating external reference calibration errors among the radars at least according to the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system.
In some possible embodiments, the external reference calibration module is further configured to extract first point cloud data of the N calibration plates in the coordinate system of the reference map and extract second point cloud data of the N calibration plates in the point cloud overlapping area of the coordinate system of the reference map by using a point cloud extraction algorithm; and the point cloud overlapping area is an overlapping area of point cloud data in the reference map coordinate system and point cloud data in the registration coordinate system.
In some possible embodiments, the error calculation module is configured to calculate, according to the first point cloud data of each radar, first normal vectors of the N calibration plates in the registered coordinate system; calculating N second normal vectors in a reference map coordinate system from the second point cloud data of each radar; and calculating the angle error of each radar from the registration coordinate system to the reference map coordinate system according to the first normal vector and the second normal vector.
In some possible embodiments, the error calculation module is configured to calculate, according to the first normal vector and the second normal vector, a deviation matrix of a rotation matrix of each radar from the radar coordinate system to the registration coordinate system and a rotation matrix of each radar from the radar coordinate system to the reference map coordinate system by using a least square method, where the deviation matrix is used to represent an angle error of each radar from the registration coordinate system to the reference map coordinate system.
In some possible embodiments, the error calculation module is configured to obtain first intersection data of S calibration boards intersecting in the N calibration boards according to the first point cloud data, where S is an integer greater than or equal to 3 and less than or equal to N; according to the second point cloud data, second intersection data of the S calibration plates in the reference map coordinate system are obtained; and calculating the displacement error of each radar from the registration coordinate system to the reference map coordinate system according to the first intersection data and the second intersection data.
In some possible embodiments, the error calculation module is configured to calculate a transformation matrix of each radar from the registration coordinate system to the reference map coordinate system according to an angle error and a displacement error of each radar from the registration coordinate system to the reference map coordinate system; determining an mth radar in the M radars as a reference radar, wherein M is a positive integer less than or equal to M; and calculating the angle error and the displacement error of each radar relative to the reference radar at least according to the transformation matrix of each radar from the registration coordinate system to the reference map coordinate system and the transformation matrix of the reference radar from the registration coordinate system to the reference map coordinate system.
In some possible embodiments, the error calculation module is further configured to calculate an angle error and a displacement error of each radar with respect to the reference radar based on the transformation matrix of each radar from the registration coordinate system to the reference map coordinate system, the transformation matrix of the reference radar from the registration coordinate system to the reference map coordinate system, and the transformation matrix of each radar coordinate system to the registration coordinate system.
In a third aspect, the present application provides an external reference calibration apparatus, including: a memory storing computer-executable instructions; a processor coupled to the memory for executing the computer-executable instructions to implement the method according to the first aspect and any possible implementation thereof.
In a fourth aspect, the present application provides a laser radar external reference calibration system, including: m radar stations and an external reference calibration apparatus as described in the third aspect and any possible embodiment thereof; n calibration plates are arranged in a view field of each radar, M is a positive integer, and N is an integer larger than or equal to 3.
In a fifth aspect, the present application provides a computer storage medium storing computer-executable instructions that, when executed by a processor, are capable of implementing the method according to the first aspect and any possible implementation manner thereof.
Compared with the prior art, the technical scheme provided by the application has the beneficial effects that:
in the method, after external reference calibration, external reference calibration errors, such as angle errors and translation errors, among the multiple radars are calculated, so that the external reference calibration precision of the multiple radars is evaluated, operators are guided to carry out quantitative judgment, and the automation efficiency is greatly improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the scope of the application.
Drawings
Fig. 1 is a schematic structural diagram of a lidar in the related art;
FIG. 2 is a schematic diagram of an external reference calibration system in an embodiment of the present application;
fig. 3 is a schematic implementation flow chart of an evaluation method of radar external reference calibration accuracy in the embodiment of the present application;
FIG. 4 is a diagram illustrating relationships between coordinate systems in an embodiment of the present application;
FIG. 5 is a schematic implementation flow chart of another method for evaluating the radar external reference calibration accuracy in the embodiment of the present application;
FIG. 6 is a diagram illustrating the relationship of transformation matrices in an embodiment of the present application;
fig. 7 is a schematic structural diagram of an apparatus for evaluating radar external reference calibration accuracy in an embodiment of the present application;
fig. 8 is a schematic structural diagram of a calibration apparatus of a laser radar in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
Lidar is a target detection technology. The laser radar emits laser beams through the laser, the laser beams are subjected to diffuse reflection after encountering a target object, the reflected beams are received through the detector, and characteristic quantities such as the distance, the direction, the height, the speed, the posture and the shape of the target object are determined according to the emitted beams and the reflected beams.
The application field of laser radars is very wide. In addition to military applications, it is now widely used in the field of life, including but not limited to: the field of intelligent piloted vehicles, intelligent piloted aircraft, three-dimensional (3D) printing, virtual reality, augmented reality, service robots, and the like. Taking an intelligent home driving technology as an example, a laser radar is arranged in an intelligent driving vehicle, and the laser radar can scan the surrounding environment by rapidly and repeatedly emitting laser beams to acquire point cloud data and the like reflecting the appearance, position and motion of one or more target objects in the surrounding environment.
The intelligent driving technology may refer to unmanned driving, automatic driving, assisted driving, and the like.
Fig. 1 is a schematic structural diagram of a laser radar in the related art, and referring to fig. 1, a laser radar 10 may include: a light emitting device 101, a light receiving device 102, and a processor 103. The light emitting device 101 and the light receiving device 102 are both connected to the processor 103.
The connection relationship among the above devices may be electrical connection or optical fiber connection. More specifically, in the light emitting device 101 and the light receiving device 102, it is also possible to include a plurality of optical devices, respectively, and the connection relationship between these optical devices may also be spatial light transmission connection.
The processor 103 is used to implement control of the light emitting device 101 and the light receiving device 102 so that the light emitting device 101 and the light receiving device 102 can operate normally. For example, the processor 103 may provide driving voltages for the light emitting device 101 and the light receiving device 102, respectively, and the processor 103 may also provide control signals for the light emitting device 101 and the light receiving device 102.
Illustratively, the processor 103 may be a general-purpose processor, such as a Central Processing Unit (CPU), a Network Processor (NP), or the like; the processor 103 may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like.
A light source (not shown in fig. 1) is also included in the light emitting device 101. It is understood that the light source may refer to a laser, and the number of lasers may be one or more. Alternatively, the laser may be a Pulsed Laser Diode (PLD), a semiconductor laser, a fiber laser, or the like. The light source is used for emitting laser beams. In particular, the processor 103 may send an emission control signal to the light source, thereby triggering the light source to emit the laser beam.
It will be appreciated that the laser beam may also be referred to as a laser pulse, a laser, an emitted beam, etc.
The detection process of the target object 104 by the lidar will be briefly described below with reference to the structure of the lidar shown in fig. 1.
Referring to fig. 1, the laser beam propagates in the emitting direction, and when the laser beam encounters the target object 104, the laser beam is reflected on the surface of the target object 104, and the reflected beam is received by the light receiving device 102 of the lidar. The beam of the laser beam reflected back by the target object 104 may be referred to herein as an echo beam (the laser beam and the echo beam are identified in fig. 1 by solid lines).
After receiving the echo light, the light receiving device 102 performs photoelectric conversion on the echo light, that is, converts the echo light into an electrical signal, the light receiving device 102 outputs the electrical signal corresponding to the echo light to the processor 103, and the processor 103 can obtain point cloud data of the shape, position, motion, and the like of the target object 104 according to the electrical signal of the echo light.
In practical applications, a single vehicle may be equipped with multiple radars in order to achieve a large field of view, even full field coverage. However, due to different installation positions, the coordinate systems of multiple radars are not uniform, so that the point clouds output by the multiple radars cannot be unified to the same coordinate system, and therefore, external references of the multiple radars need to be calibrated to output point cloud data under the same coordinate system.
In the related technology, on the premise of obtaining high-precision point cloud data of a calibration scene (map) in advance, radar coordinate systems of a plurality of radars are respectively registered to a map coordinate system to realize multi-radar external reference calibration. Therefore, the quality and the precision of the actually spliced point cloud data depend on the precision of external reference calibration. However, how to evaluate the accuracy of the multi-radar external reference calibration is an urgent problem to be solved.
In order to solve the above problem, an embodiment of the present application provides a method for evaluating radar external reference calibration precision, where the method may be applied to the external reference calibration system of the laser radar. Fig. 2 is a schematic diagram of an external reference calibration system in an embodiment of the present application, and referring to fig. 2, the external reference calibration system 20 may include: the radar external reference calibration method comprises the following steps of M radars (which can be recorded as radar 1, radar 2, radar 3, …, radar M (M is a positive integer), external reference calibration equipment 22 and calibration plates 23, wherein the M radars are radars which need external reference calibration, the external reference calibration equipment 22 is used for conducting external reference calibration on the M radars and evaluating external reference calibration precision, N calibration plates 23(N is a positive integer and N is an integer larger than or equal to 3) are arranged in a field of view of each radar, the N calibration plates 23 are used for external reference calibration of each radar and evaluation of the external reference calibration precision, and optionally, the N calibration plates 23 at least comprise S calibration plates which are intersected pairwise, and S is an integer larger than or equal to 3 and smaller than or equal to N.
In the embodiments of the present application, the radar refers to a laser radar, unless otherwise specified.
In practical applications, the external reference calibration device may be an independent device, or may be integrated with a radar, which is not specifically limited in this embodiment of the present application.
The method for evaluating the radar external reference calibration precision provided by the embodiment of the present application is described below with reference to the external reference calibration system.
Fig. 3 is a schematic implementation flow chart of an evaluation method for radar external reference calibration accuracy in an embodiment of the present application, and referring to fig. 3, the method may include:
s301, obtaining a registration coordinate system of M radars.
The reference map coordinate system is a self coordinate system established when the external reference calibration equipment works. The registration coordinate system is obtained based on point cloud registration in the radar external reference calibration process. The registered coordinate system may be understood as a measured value of the reference map coordinate system, rather than the actual value of the reference map coordinate system.
For example, fig. 4 is a schematic diagram of the relationship between various coordinate systems in the embodiment of the present application, and referring to fig. 4, a radar coordinate system OiN vectors of
By passing from OiTo the registration coordinate system OguessOf (3) a rotation matrix RguessMapping to OguessTo obtain corresponding N vectors
N vectors
By passing from OiTo reference map coordinate system OmapOf (3) a rotation matrix RmapMapping to OmapTo obtain corresponding N vectors
It can be understood that the external reference calibration device may perform external reference calibration on the M radars first to calculate a transformation matrix of each radar from the respective radar coordinate system to the reference map coordinate system. However, due to the existence of calculation errors, the calculated transformation matrix is actually the transformation matrix from the respective radar coordinate system to the registration coordinate system. Therefore, the registration coordinate system of each radar is obtained through the transformation matrix calculated by external reference calibration.
For example, for a radar i (i.e. the ith radar, i is 1, 2, …, M) in M radars, the external reference calibration device performs external reference calibration to obtain a radar coordinate system O of the radar iiRegistered coordinate system O to radar iguess,iIs transformed by the transformation matrix Tguess,i。
In some possible embodiments, the external reference calibration device may perform external reference calibration on multiple radars by using a point cloud registration algorithm. By way of example, the point cloud registration algorithm described above may include, but is not limited to: iterative closest point algorithm (ICP), Normal Distribution Transform (NDT).
S302, first point cloud data and second point cloud data of the M radars are obtained.
Here, the first point cloud data may be point cloud data of N calibration plates in each radar field of view in the registered coordinate system. The second point cloud data is the point cloud data of N calibration plates in each radar field of view in the reference map coordinate system. The second point cloud data may also be understood as point cloud data of the N calibration plates in a point cloud overlapping area of the reference map coordinate system.
It should be noted that the point cloud overlapping region is an overlapping region of point cloud data in the reference map coordinate system and point cloud data in the registration coordinate system, and is a set of point cloud data. It can also be understood that the point cloud data in the registration coordinate system is mapped to the point cloud data in the reference map coordinate system.
In a possible implementation, the S302 may include: and extracting first point cloud data of the N calibration plates in a registration coordinate system and extracting second point cloud data of the N calibration plates in a point cloud overlapping area of a reference map coordinate system by adopting a point cloud extraction algorithm.
For example, the point cloud extraction algorithm may include, but is not limited to: random sample consensus (RANSAC) algorithm.
For example, for radar i, the external reference calibration apparatus may use RANSAC algorithm to calibrate the coordinate system O of radar iguess,iExtracting point cloud data (i.e., first point cloud data) of a calibration plate (i.e., N calibration plates within the field of view of the radar i, e.g., N ═ 3), and extracting from the point cloud data of OmapIn the point cloud overlapping area of (a), point cloud data (i.e., second point cloud data) of the calibration plate is extracted.
And S303, calculating an angle (such as an axis angle) error and a displacement error of each radar from the registration coordinate system to the reference map coordinate system according to the first point cloud data and the second point cloud data.
In some possible embodiments, the above S303 may be regarded as two calculation processes, namely, calculating an angle error of each radar from the registration coordinate system to the reference map coordinate system and calculating a displacement error of each radar from the registration coordinate system to the reference map coordinate system. Then, fig. 5 is a schematic implementation flow chart of another method for evaluating the radar external reference calibration accuracy in the embodiment of the present application, and referring to fig. 5, S303 may include:
s501, calculating an angle error of each radar from a registration coordinate system to a reference map coordinate system according to the first point cloud data and the second point cloud data;
and S502, calculating the displacement error of each radar from the registration coordinate system to the reference map coordinate system according to the first point cloud data and the second point cloud data.
It should be noted that S501 and S502 may be sequentially executed, for example, S501 is executed first and then S502 is executed, and S502 is executed first and then S501 is executed; or may be performed simultaneously. Of course, the execution timing of S501 and S502 is not particularly limited in the embodiment of the present application.
In some possible embodiments, S501 may include: calculating first normal vectors of the N calibration plates in the field of view of each radar in a registration coordinate system according to the first point cloud data of each radar, and calculating second normal vectors of the N calibration plates in a reference map coordinate system according to the second point cloud data of each radar; and calculating the angle error of each radar from the registration coordinate system to the reference map coordinate system according to the first normal vector and the second normal vector.
In the embodiment of the present application, each of the M radars has to perform S501 to S502. For convenience of description, S501 and S502 will be described below by taking, as an example, a radar i (i is 1, 2, 3, …, M) among M radars.
It can be understood that, for radar i, after obtaining the first point cloud data and the second point cloud data of radar i through S302, the external reference calibration device may calculate a first normal vector, that is, a field of view of radar i, according to the first point cloud dataInner N calibration plates are at Oguess,iNormal vector K ini(can be described as
). And the external reference calibration equipment can calculate a second normal vector according to the second point cloud data, namely N calibration plates in the field of view of the radar i in the O directionmapNormal vector J in (1)i(can be described as
)。
Further, the external reference calibration device can be based on KiAnd JiCalculating the radar i from O using the least squares methodiTo Oguess,iOf (3) a rotation matrix Rguess,iAnd OiTo OmapOf (3) a rotation matrix Rmap,iDeviation matrix W ofguess，i. Here, Wguess，iFor indicating radar i from Oguess,iTo OmapThe angle error of (2).
Illustratively, assume at OiIn the field of view of the radar i, the normal vectors of N calibration plates can be recorded as
External reference calibration equipment obtaining KiAnd JiThen, the following formulas (1) to (2) can be obtained, wherein Rguess,iIs OiTo Oguess,iOf a rotation matrix Rmap,iIs OiTo OmapThe rotation matrix of (2).
Here, Rguess,iAnd Rmap,iW for deviation matrixguess，iExpressed, then, by the formulas (1) to (2), the formula (3) can be obtained.
Further, according to the least square method, a deviation matrix W is obtained through calculationguess，i(also referred to as an angle deviation matrix) as shown in equation (4).
Ki＝Rguess,iPi (1)
Ji＝Rmap,iPi (2)
Wherein the content of the first and second substances,
is KiThe transpose matrix of (a) is,
is JiThe transpose matrix of (a) is,
is composed of
The inverse of the matrix of (a) is,
the transposed matrix of (2).
In some possible embodiments, W is as defined aboveguess，iSingular Value Decomposition (SVD) decomposition may also be performed according to equation (5) to perform Wguess，iAnd (6) optimizing.
Wherein, UiBeing an orthogonal matrix, DiAs a diagonal matrix, ViIs an orthogonal matrix, and the matrix is,
is a ViThe transposed matrix of (2); SVD () is an SVD decomposition operation, "→" represents a formula derivation.
In practical application, the steps are executed for each radar, and an angular deviation matrix, namely W, from the registration coordinate system to the reference map coordinate system of each radar can be calculatedguess，1、Wguess，2、…、Wguess，M。
In some possible embodiments, S502 may include: obtaining first intersection data of S calibration plates intersected in the N calibration plates according to the first point cloud data, wherein S is a positive integer less than or equal to N (for example, S is 3); according to the second point cloud data, second intersection data of the S calibration plates in the reference map coordinate system are obtained; and calculating the displacement error of each radar from the registration coordinate system to the reference map coordinate system according to the first intersection data and the second intersection data.
It can be understood that, still taking radar i as an example, after obtaining the first point cloud data and the second point cloud data of radar i through S302, the external reference calibration apparatus may obtain intersection data (i.e., the first intersection data, which may be recorded as the first intersection data) of S (e.g., S ═ 3) calibration boards that intersect each other in pairs among the N calibration boards according to the first point cloud data
n is a positive integer). And the external reference calibration equipment can calculate S calibration plates which are intersected pairwise in O according to the second point cloud datamapThe intersection data in (i.e., the second intersection data, can be written as
). The external reference calibration apparatus may then follow equation (6) below, based on
And
computing radar i from Oguess,iTo OmapDisplacement error of
In practical application, the steps are executed for each radar, and the displacement error of each radar from the registration coordinate system to the reference map coordinate system can be calculated, namely
In the embodiment of the present application, the radar i is from Oguess,iTo OmapIs transformed by the transformation matrix TiCan be expressed as the following equation (7):
in practical application, for each radar, a transformation matrix, namely T, from the registration coordinate system to the reference map coordinate system of each radar can be calculated1、T2、…、Ti、…、TM。
Thus, for each radar, the external reference calibration apparatus can calculate the angle error and the displacement error of each radar from the respective registration coordinate system with respect to the reference map coordinate system by executing the above-described S501 to S502.
S304, obtaining an external reference calibration error between each radar at least according to the angle error and the displacement error of each radar relative to the reference map coordinate system from the respective registration coordinate system.
As can be understood, through S301 to S303, the external reference calibration apparatus calculates an angular error and a displacement error of the registration coordinate system of each radar with respect to the reference map coordinate system. After that, in order to evaluate the external reference calibration precision of each radar, the external reference calibration device needs to further calculate the external reference calibration error between each radar, that is, the radar coordinate systems of each radar are mapped to the external reference calibration error under the radar coordinate system of the reference radar in a unified manner. Then, S304 may include: calculating a transformation matrix of each radar from the registration coordinate system to the reference map coordinate system according to the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system; determining an M-th radar (M is an integer greater than or equal to 3 and less than or equal to M) in the M radars as a reference radar; and calculating the external reference calibration error between each radar at least according to the transformation matrix of each radar from the respective registration coordinate system to the reference map coordinate system and the transformation matrix of the reference radar from the respective registration coordinate system to the reference map coordinate system. Here, the external reference calibration error between each radar refers to an angle error and a displacement error of each radar with respect to the reference radar.
In some possible embodiments, the step of calculating the external reference calibration error between each radar may be implemented by:
for example, the external reference calibration device selects radar 1 (i.e., mth radar) as the reference radar.
Accordingly, the slave O of the radar i after registration by the radar 1 is calculated, see the following equations (8) and (9)iTo Oguess,iIs transformed by the transformation matrix Tguess,1iAnd slave O of radar i after registration of radar 1iTo OmapIs transformed by the transformation matrix Tmap,1i。
Wherein, fig. 6 is a schematic diagram of the relationship between various transformation matrices in the embodiment of the present application, see fig. 6, Tguess,1As a radar coordinate system O from the radar 11Registration coordinate system O to radar 1guess,1Of the transformation matrix, Tmap,1Is from O1To OmapThe transformation matrix of (2); t isguess,iIs from OiTo Oguess,iTransformation moment ofArray, Tmap,iIs from OiTo OmapOf the transformation matrix, Tguess,1iCan also be understood as being from O1To OiOf the transformation matrix, Tmap,1iCan also be understood as being from O1To OiThe true value of the transformation matrix.
Further, Tguess,1iAnd Tmap,1iDeviation matrix T of1iSee the following equation (10):
wherein the content of the first and second substances,
is Tguess,1iThe inverse matrix of (c).
Further, the following formulas (11) and (12) may exist:
wherein, T1For radar 1 by Oguess,1To OmapOf the transformation matrix, TiFor radar i by Oguess,iTo OmapThe transformation matrix of (2).
Then, the external reference calibration apparatus substitutes equations (8), (9), (11) and (12) into equation (10) to obtain equation (13), i.e., Tguess,1iAnd Tmap,1iDeviation matrix T of1iThe following were used:
wherein, W1iIs from O1To OiThe angular deviation matrix of (a) is,
is from O1To OiDisplacement error of (2).
Further, the external reference calibration device can obtain the radar i from O according to the formula (13)1To OiSee equation (14):
θ1i＝arccos((Tr(W1i)-1)/2) (14)
wherein Tr () is a trace operation.
In some possible embodiments, W1iThe SVD decomposition may be performed according to equation (14) to W1iAnd (6) optimizing.
Wherein, U1iBeing an orthogonal matrix, D1iAs a diagonal matrix, V1iIs an orthogonal matrix, and the matrix is,
is a V1iThe transposed matrix of (2).
In the embodiment of the present application, each radar performs the above steps to obtain T of each radar12、T13…、T1i...、T1MThe external reference calibration error among the radars, namely the external reference calibration angle error W of each radar relative to the radar 1 is obtained1iAnd displacement error
In some possible embodiments, the external reference calibration apparatus may repeatedly perform the above steps S301 to S304 for multiple times to obtain multiple external reference calibration errors for each radar, and then perform statistical analysis on the external reference calibration errors to obtain a statistical analysis result that reflects the external reference calibration accuracy, such as a pie chart, a histogram, and an error bar (error bar).
Therefore, the evaluation process of the external parameter calibration precision of the M radars is completed.
In the embodiment of the application, the external reference calibration precision of the multiple radars is evaluated by calculating the angle error and the translation error of the multiple radars from the calibration coordinate system after external reference calibration to the reference map coordinate system.
Based on the same inventive concept, the embodiment of the present application provides an evaluation device for radar external reference calibration precision, which may be an external reference calibration device of a laser radar, or a chip or a system on a chip in the external reference calibration device, or a functional module in the external reference calibration device of the laser radar for implementing the method described in each of the embodiments. The calibration device can implement the functions executed by the calibration device in the above embodiments, and these functions can be implemented by hardware executing corresponding software. These hardware or software include one or more functionally corresponding modules. Fig. 7 is a schematic structural diagram of an apparatus for evaluating radar external reference calibration accuracy in an embodiment of the present application, and referring to fig. 7, the apparatus 700 may include: the external reference calibration module 701 is used for obtaining a registration coordinate system, first point cloud data and second point cloud data of the M radars based on point cloud registration, wherein the registration coordinate system is a reference map coordinate system obtained based on radar external reference calibration, the first point cloud data is the point cloud data of the N calibration plates in each radar field in the registration coordinate system, and the second point cloud data is the point cloud data of the N calibration plates in the reference map coordinate system; an error calculation module 702, configured to calculate an angle error and a displacement error of each radar from the registration coordinate system to the reference map coordinate system according to the first point cloud data and the second point cloud data; and calculating external reference calibration errors among the radars at least according to the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system.
In some possible embodiments, the external reference calibration module 701 is further configured to extract first point cloud data of the N calibration plates in the coordinate system of the reference map and extract second point cloud data of the N calibration plates in the point cloud overlapping area of the coordinate system of the reference map by using a point cloud extraction algorithm; and the point cloud overlapping area is an overlapping area of point cloud data in the reference map coordinate system and point cloud data in the registration coordinate system.
In some possible embodiments, the error calculation module 702 is configured to calculate, according to the first point cloud data of each radar, first normal vectors of the N calibration plates in the registered coordinate system; calculating N second normal vectors in a reference map coordinate system from the second point cloud data of each radar; and calculating the angle error of each radar from the registration coordinate system to the reference map coordinate system according to the first normal vector and the second normal vector.
In some possible embodiments, the error calculation module 702 is configured to calculate, according to the first normal vector and the second normal vector, a deviation matrix of a rotation matrix of each radar from the radar coordinate system to the reference coordinate system and a rotation matrix of each radar from the radar coordinate system to the reference map coordinate system by using a least square method; the deviation matrix is used for representing the angle error of each radar from the registration coordinate system to the reference map coordinate system.
In some possible embodiments, the error calculation module 702 is configured to obtain first intersection data of S calibration boards intersecting in the N calibration boards according to the first point cloud data, where S is an integer greater than or equal to 3 and less than or equal to N; according to the second point cloud data, second intersection data of the S calibration plates in the reference map coordinate system; and calculating the displacement error of each radar from the registration coordinate system to the reference map coordinate system according to the first intersection data and the second intersection data.
In some possible embodiments, the error calculation module 702 is further configured to calculate a transformation matrix from the registration coordinate system to the reference map coordinate system for each radar according to the angular error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system; determining an mth radar in the M radars as a reference radar, wherein the value of M is 1, 2, 3, … and M; and calculating the angle error and the displacement error of each radar relative to the reference radar at least according to the transformation matrix of each radar from the registration coordinate system to the reference map coordinate system and the transformation matrix of the reference radar from the registration coordinate system to the reference map coordinate system.
In some possible embodiments, the error calculation module 702 is further configured to calculate an angle error and a displacement error of each radar with respect to the reference radar according to a transformation matrix of each radar from the registration coordinate system to the reference map coordinate system, a transformation matrix of the reference radar from the registration coordinate system to the reference map coordinate system, and a transformation matrix of each radar coordinate system to the registration coordinate system.
It should be noted that, for the specific implementation process of the external reference calibration module 701 and the error calculation module 702, reference may be made to the detailed description of the embodiments in fig. 2 to fig. 6, and for brevity of the description, no further description is given here.
The external reference calibration module 701 and the error calculation module 702 mentioned in the embodiments of the present application may be one or more processors.
Based on the same inventive concept, embodiments of the present application provide a calibration device for a laser radar, where the calibration device may be an external reference calibration device described in one or more embodiments above. Fig. 8 is a schematic structural diagram of an external reference calibration apparatus of a laser radar in an embodiment of the present application, and referring to fig. 8, a calibration apparatus 800 may employ general-purpose computer hardware, which includes a processor 801 and a memory 802.
Alternatively, the processor 801 and the memory 802 may communicate via a bus 803.
In some possible implementations, the at least one processor 801 may constitute any physical device having circuitry to perform logical operations on one or more inputs. For example, at least one processor may include one or more Integrated Circuits (ICs), including an Application Specific Integrated Circuit (ASIC), a microchip, a microcontroller, a microprocessor, all or part of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or other circuitry suitable for executing instructions or performing logical operations. The instructions executed by the at least one processor may be preloaded into a memory integrated with or embedded in the controller, for example, or may be stored in a separate memory. The memory may include Random Access Memory (RAM), read-only memory (ROM), hard disk, optical disk, magnetic media, flash memory, other persistent, fixed, or volatile memory, or any other mechanism capable of storing instructions. In some embodiments, the at least one processor may comprise more than one processor. Each processor may have a similar structure, or the processors may have different configurations that are electrically connected or disconnected from each other. For example, the processor may be a separate circuit or integrated in a single circuit. When more than one processor is used, the processors may be configured to operate independently or cooperatively. The processors may be coupled electrically, magnetically, optically, acoustically, mechanically or by other means allowing them to interact. According to an embodiment of the present application, there is also provided a computer readable storage medium having stored thereon computer instructions for executing the steps of the above calibration method by a processor. The memory 802 may include computer storage media in the form of volatile and/or nonvolatile memory such as read only memory and/or random access memory. Memory 802 may store an operating system, application programs, other program modules, executable code, program data, user data, and the like.
In addition, the memory 802 stores computer-executable instructions for implementing the functions of the external parameter calibration module 701 and the error calculation module 702 in fig. 7. The functions/implementation processes of the external reference calibration module 701 and the error calculation module 702 in fig. 7 can be implemented by the processor 801 in fig. 8 calling the computer-executed instructions stored in the memory 802, and the specific implementation processes and functions refer to the above-described related embodiments.
Based on the same inventive concept, the embodiment of the present application provides an external reference calibration apparatus for a laser radar, including: a memory storing computer-executable instructions; and the processor is connected with the memory and used for executing the computer executable instructions and realizing the method for evaluating the radar external reference calibration precision according to one or more of the embodiments.
Based on the same inventive concept, the present application provides a computer storage medium, where computer-executable instructions are stored, and after the computer-executable instructions are executed by a processor, the method for evaluating the radar external reference calibration accuracy according to one or more embodiments can be implemented.
It should be understood by those skilled in the art that the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for evaluating the calibration accuracy of radar external parameters is characterized by comprising the following steps:
acquiring a registration coordinate system, first point cloud data and second point cloud data of M radars based on point cloud registration, wherein the first point cloud data are point cloud data of N calibration plates in each radar field of view in the registration coordinate system, the second point cloud data are point cloud data of the N calibration plates in a reference map coordinate system, M is a positive integer, and N is an integer greater than or equal to 3;
calculating an angle error and a displacement error of each radar from a registration coordinate system to a reference map coordinate system according to the first point cloud data and the second point cloud data;
and calculating external reference calibration errors among the radars at least according to the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system.
2. The method of claim 1, wherein the obtaining first point cloud data and second point cloud data for the M radars comprises:
extracting the first point cloud data of the N calibration plates in a registration coordinate system and extracting the second point cloud data of the N calibration plates in a point cloud overlapping area of a reference map coordinate system by adopting a point cloud extraction algorithm;
and the point cloud overlapping area is an overlapping area of point cloud data in the reference map coordinate system and point cloud data in the registration coordinate system.
3. The method according to claim 1 or 2, wherein the calculating an angle error of each radar from a registration coordinate system to a reference map coordinate system according to the first point cloud data and the second point cloud data comprises:
calculating first normal vectors of the N calibration plates in a registration coordinate system according to the first point cloud data of each radar;
calculating second normal vectors of the N calibration plates in a reference map coordinate system according to the second point cloud data of each radar;
and calculating the angle error of each radar from the registration coordinate system to the reference map coordinate system according to the first normal vector and the second normal vector.
4. The method of claim 3, wherein calculating the angular error of each radar from the reference coordinate system to the reference map coordinate system based on the first normal vector and the second normal vector comprises:
and calculating a deviation matrix of a rotation matrix of each radar from a radar coordinate system to a registration coordinate system and a rotation matrix of each radar from the radar coordinate system to a reference map coordinate system by using a least square method according to the first normal vector and the second normal vector, wherein the deviation matrix is used for representing the angle error of each radar from the registration coordinate system to the reference map coordinate system.
5. The method of claim 1, wherein calculating a displacement error of each radar from a registration coordinate system to a reference map coordinate system according to the first point cloud data and the second point cloud data comprises:
obtaining first intersection data of S calibration plates intersected in the N calibration plates according to the first point cloud data, wherein S is an integer which is greater than or equal to 3 and less than or equal to N;
according to the second point cloud data, second intersection data of the S calibration plates in a reference map coordinate system are obtained;
and calculating the displacement error of each radar from the coordinate system of the registration map to the coordinate system of the reference map according to the first intersection data and the second intersection data.
6. The method of claim 1, wherein calculating the external reference calibration error between each radar according to the angle error and the displacement error of each radar from the calibration coordinate system to the reference map coordinate system comprises:
calculating a transformation matrix of each radar from the registration coordinate system to the reference map coordinate system according to the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system;
determining an mth radar in the M radars as a reference radar, wherein M is a positive integer less than or equal to M;
and calculating the angle error and the displacement error of each radar relative to the reference radar at least according to the transformation matrix of each radar from the registration coordinate system to the reference map coordinate system and the transformation matrix of the reference radar from the registration coordinate system to the reference map coordinate system.
7. The method of claim 6, wherein calculating the angular error and the displacement error of each radar relative to the reference radar based on at least a transformation matrix of each radar from a registered coordinate system to a reference map coordinate system and a transformation matrix of the reference radar from a registered coordinate system to a reference map coordinate system comprises:
and calculating the angle error and the displacement error of each radar relative to the reference radar according to the transformation matrix of each radar from the registration coordinate system to the reference map coordinate system, the transformation matrix of the reference radar from the registration coordinate system to the reference map coordinate system and the transformation matrix of each radar coordinate system to the registration coordinate system.
8. An evaluation device for radar external reference calibration precision is characterized by comprising:
the external reference calibration module is used for obtaining a registration coordinate system, first point cloud data and second point cloud data of M radars based on point cloud registration, wherein the registration coordinate system is a reference map coordinate system obtained based on radar external reference calibration, the first point cloud data are point cloud data of N calibration plates in each radar field in the registration coordinate system, the second point cloud data are point cloud data of the N calibration plates in the reference map coordinate system, M is a positive integer, and N is an integer greater than or equal to 3;
the error calculation module is used for calculating an angle error and a displacement error of each radar from a registration coordinate system to a reference map coordinate system according to the first point cloud data and the second point cloud data; and calculating external reference calibration errors among the radars at least according to the angle error and the displacement error of each radar from the registration coordinate system to the reference map coordinate system.
9. An external reference calibration device, comprising:
a memory storing computer-executable instructions;
a processor coupled to the memory for executing the computer-executable instructions to implement the method of any of claims 1 to 7.
10. A computer storage medium having computer-executable instructions stored thereon which, when executed by a processor, are capable of implementing the method of any one of claims 1 to 7.
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