Calibration Method, System and Device for an Autonomous Mobile Platform Based on Planar Motion

Abstract

A calibration method, system, and device for an autonomous mobile platform based on planar motion. The calibration method includes the steps of: S100: obtaining radar data and locator data collected simultaneously by a radar and a locator mounted on the autonomous mobile platform performing planar motion; S200: obtaining a radar transformation matrix and a locator transformation matrix for each time period based on the radar data and the locator data; S300: calibrating the rotation angles in two dimensions during the process of transforming from the locator coordinate system to the radar coordinate system according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period; and S400: calibrating the remaining parameters of the relative pose between the radar and the locator based on the calibrated rotation angles and the radar transformation matrix and the locator transformation matrix in the same time period.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous mobile platforms, and more particularly to a calibration method, system and device for an autonomous mobile platform based on planar motion. Background Art

[0002] Currently, with the development of artificial intelligence technology, more and more companies and university teams are focusing on the research of autonomous mobile platforms. An autonomous mobile platform generally refers to a vehicle equipped with a lot of sensors, which can achieve autonomous perception and movement, and then complete corresponding tasks through the task modules carried thereon, such as an inspection vehicle or a floor cleaning robot, etc. The autonomous mobile platform has also attracted more and more research teams due to its advantages of reducing labor costs and minimizing human contact.

[0003] Since an autonomous mobile platform usually needs to handle a variety of complex tasks, and according to the task requirements, the autonomous mobile platform usually needs to jointly use a radar and a locator, it is necessary to perform joint calibration on the radar and the locator on the autonomous mobile platform before it is put into use. It can be understood that the radar carried on the autonomous mobile platform generally refers to a laser detection and ranging system (English: Light Detection and Ranging; abbreviated as LiDAR), which detects information such as the position and speed of a target by emitting laser beams. The locator carried on the autonomous mobile platform generally refers to various types of navigation positioning systems such as a Beidou navigation system locator or a GPS locator, etc., which are used to obtain the pose, orientation and speed, etc. of the autonomous mobile platform.

[0004] However, although current academic research on radar and locator calibration methods mostly focuses on hand-eye calibration or improved schemes of hand-eye calibration, these calibration methods require a large amount of rotation in each coordinate axis (such as the x-axis, y-axis and z-axis) of the autonomous mobile platform (such as a robot or an intelligent vehicle), which is not practical for some projects that require the autonomous mobile platform to only perform planar motion (such as a floor cleaning robot that only moves on a horizontal ground), and cannot be applied. For example, the existing two-step method is to first solve the rotation matrix, and then orthogonalize the rotation matrix and then solve the translation matrix, but its disadvantage is that it cannot be applied to robots or intelligent vehicles that only perform planar motion; the existing mathematical method is to solve the hand-eye relationship through mathematical tools, which has higher accuracy than the two-step method, but is extremely unstable in the case of obvious noise; the existing motion restriction method is to solve hand-eye calibration by restricting the motion of the robot or intelligent vehicle, but it is very difficult to implement in a real scenario and requires a specific motion trajectory and extremely high motion accuracy; although the existing non-linear method first calculates the initial value of the quaternion and then solves the final solution through non-linear optimization, this method is complex to implement, has weak practicability, and requires other methods to calculate the initial value first. Summary of the Invention

[0005] One advantage of the present invention is to provide a calibration method, system and device for an autonomous mobile platform based on planar motion, which can complete the calibration of the radar and locator mounted on the autonomous mobile platform when the autonomous mobile platform only performs planar motion.

[0006] Another advantage of the present invention is to provide a calibration method, system and device for an autonomous mobile platform based on planar motion. In one embodiment of the present invention, the calibration method for the autonomous mobile platform based on planar motion no longer requires the autonomous mobile platform to have a fixed trajectory and strict pose during the calibration process, which helps to simplify the calibration process and reduce the restrictions on the autonomous mobile platform.

[0007] Another advantage of the present invention is to provide a calibration method, system and device for an autonomous mobile platform based on planar motion. In one embodiment of the present invention, the calibration method for the autonomous mobile platform based on planar motion can first calibrate the rotation angles in two dimensions to correct the motion process into a true planar motion, so that the automatic mobile platform does not require a fixed trajectory and strict pose, and only needs to perform simple planar motion to be calibrated.

[0008] Another advantage of the present invention is to provide a calibration method, system and device for an autonomous mobile platform based on planar motion. In one embodiment of the present invention, the calibration method for the autonomous mobile platform based on planar motion does not need to rely on a calibration board and has no strict requirements on the external environment, which helps to be applicable to various complex scenarios.

[0009] Another advantage of the present invention is to provide a calibration method, system and device for an autonomous mobile platform based on planar motion. In one embodiment of the present invention, the calibration method for the autonomous mobile platform based on planar motion can calculate the pose change by means of a point cloud registration scheme and a positioning difference scheme, so that it does not need to rely on a calibration board to be suitable for any complex calibration environment.

[0010] Another advantage of the present invention is to provide a calibration method, system and device for an autonomous mobile platform based on planar motion. In one embodiment of the present invention, the calibration method for the autonomous mobile platform based on planar motion does not need to assign an initial value to the parameter to be calibrated, which helps to simplify the calibration process and reduce the calibration difficulty.

[0011] Another advantage of the present invention is to provide a calibration method, system and device for an autonomous mobile platform based on planar motion. In one embodiment of the present invention, the calibration method for the autonomous mobile platform based on planar motion can reasonably reduce the interference of noise, which helps to make the calibration method have stronger anti-noise ability.

[0012] Another object of the present invention is to provide a calibration method, system and device for an autonomous mobile platform based on planar motion. In order to achieve the above object, in the present invention, complex structures or algorithms are not required. Therefore, the present invention successfully and effectively provides a solution, which not only provides a simple calibration method, system and device for an autonomous mobile platform based on planar motion, but also increases the practicability and reliability of the calibration method, system and device for the autonomous mobile platform based on planar motion.

[0013] In order to achieve the above at least one object of the invention or other objects and advantages, the present invention provides a calibration method for an autonomous mobile platform based on planar motion, including the steps of:

[0014] S100: Obtain radar data and locator data collected simultaneously by a radar and a locator carried on the autonomous mobile platform performing planar motion.

[0015] S200: Obtain a radar transformation matrix and a locator transformation matrix in each time period according to the radar data and the locator data, where the radar transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the radar in each time period, and the locator transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the locator in each time period;

[0016] S300: Calibrate the rotation angles in two dimensions during the process of transforming from the locator coordinate system to the radar coordinate system according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period; and

[0017] S400: Calibrate the remaining parameters of the relative pose between the radar and the locator according to the calibrated rotation angles and the radar transformation matrix and the locator transformation matrix in the same time period.

[0018] According to an embodiment of the present application, in the step S100: The autonomous mobile platform carrying the radar and the locator performs S-shaped planar motion on a horizontal plane.

[0019] According to an embodiment of the present application, the step S200 includes the steps of:

[0020] Calculate the locator transformation matrix of the locator in each time period by obtaining the transformation relationship from the locator coordinate system to the world coordinate system at each moment according to the locator data; and

[0021] Calculate the radar transformation matrix of the radar in each time period by using the locator transformation matrix as an initial value and obtaining the result of point cloud matching of the radar data by using the normal distribution transformation method and the iterative closest point method.

[0022] According to an embodiment of the present application, the step S300 includes the steps of:

[0023] Setting the transformation from the locator coordinate system to the radar coordinate system to be a rotation successively around the Z-axis, Y-axis, and Z-axis; and

[0024] According to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator within the same time period, calculating the rotation vectors successively rotating around the Z-axis and Y-axis during the transformation from the locator coordinate system to the radar coordinate system through the YZ angle calibration model, so as to obtain the angles of rotation around the Z-axis and Y-axis respectively.

[0025] According to an embodiment of the present application, the YZ angle calibration model is implemented as:

[0026]

[0027] Wherein:

[0028]

[0029] Wherein in the above formula, s represents the sine sin, and c represents the cosine cos; and there are two constraint conditions as follows:

[0030] q YZ1 q YZ4 =q YZ2 q YZ3 , ||q YZ || = 1;

[0031] Wherein q YZ represents the rotation vector of the successive rotation around the Z-axis and Y-axis from the locator coordinate system to the radar coordinate system, that is, the quaternion q YZ = [q YZ1 , q YZ2 , q YZ3 , q YZ4 , and correspondingly, the angle of rotation around the Y-axis is β and the angle of rotation around the Z-axis is γ; represents the rotation vector of the radar during the time period from time i to time i + 1; represents the rotation vector of the locator during the time period from time i to time i + 1; represents the angle of rotation of the locator during the time period from time i to time i + 1; represents the angle of rotation of the radar during the time period from time i to time i + 1; represents the rotation vector that minimizes the YZ angle calibration model.

[0032] According to an embodiment of the present application, the step S300 includes the steps of:

[0033] The transformation of setting the coordinate system of the locator to the coordinate system of the radar is to rotate successively around the X-axis, Y-axis, and Z-axis; and

[0034] According to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator within the same time period, the rotation vectors of rotating around the X-axis and Y-axis successively during the process of transforming from the locator coordinate system to the radar coordinate system are calculated through the YX angle calibration model, so as to obtain the angles of rotating around the X-axis and Y-axis respectively.

[0035] According to an embodiment of the present application, the YX angle calibration model is implemented as:

[0036]

[0037] Where:

[0038]

[0039] Where in the above formula, s represents sine sin, and c represents cosine cos; and there are two constraint conditions as follows:

[0040] q YX1 q YX4 =q YX2 q YX3 , ||q YX || = 1;

[0041] Where q YX represents the rotation vector of rotating around the X-axis and Y-axis successively from the locator coordinate system to the radar coordinate system, that is, the quaternion q YX =[q YX1 , q YX2 , q YX3 , q YX4 , and the corresponding angle of rotating around the Y-axis is β and the angle of rotating around the X-axis is γ; represents the rotation vector of the radar during the time period from time i to time i + 1; represents the rotation vector of the locator during the time period from time i to time i + 1; represents the angle of rotation of the locator during the time period from time i to time i + 1; represents the angle of rotation of the radar during the time period from time i to time i + 1; represents the rotation vector that minimizes the YX angle calibration model.

[0042] According to an embodiment of the present application, in the step S400:

[0043] Based on the translation transformation relationship during the same time period and the translation transformation relationship of the locator, the remaining relative pose parameters during the process of transforming from the locator coordinate system to the radar coordinate system are calculated through a translation calibration model.

[0044] According to an embodiment of the present application, the translation calibration model is implemented as:

[0045]

[0046]

[0047]

[0048] Where: represents the rotation angle of the locator during the time period from time i to time i + 1; t m represents the first two dimensions of the translation vector of the locator during the time period from time i to time i + 1; α represents the angle of the final rotation around the Z-axis during the transformation from the locator coordinate system to the radar coordinate system; t X represents the translation amount along the X-axis during the transformation from the locator coordinate system to the radar coordinate system; t Y represents the translation amount along the Y-axis during the transformation from the locator coordinate system to the radar coordinate system.

[0049] According to an embodiment of the present application, the calibration method for the autonomous mobile platform based on planar motion further includes the steps of:

[0050] S500: Optimize all the calibrated parameters through the nonlinear least squares method to obtain the optimal solution of the calibration parameters.

[0051] According to another aspect of the present application, the present application further provides a calibration system for an autonomous mobile platform based on planar motion, which is used to calibrate the radar and the locator carried on the autonomous mobile platform, wherein the calibration system for the autonomous mobile platform based on planar motion includes components that are communicatively connected to each other:

[0052] A data acquisition module, which is used to acquire radar data and locator data collected simultaneously by the radar and the locator carried on the autonomous mobile platform that is moving in a planar motion;

[0053] A matrix obtaining module, which is used to obtain a radar transformation matrix and a locator transformation matrix in each time period according to the radar data and the locator data, wherein the radar transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the radar in each time period, and the locator transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the locator in each time period;

[0054] A rotation calibration module, configured to calibrate the rotation angles of two dimensions in the process of transforming from the locator coordinate system to the radar coordinate system according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period; and

[0055] A translation calibration module, configured to calibrate the remaining parameters of the relative pose between the radar and the locator according to the calibrated rotation angles and the radar transformation matrix and the locator transformation matrix in the same time period.

[0056] According to an embodiment of the present application, the matrix acquisition module includes a locator matrix calculation module and a radar matrix calculation module that are communicatively connected to each other, wherein the locator matrix calculation module is configured to calculate the locator transformation matrix of the locator in each time period by obtaining the transformation relationship from the locator coordinate system to the world coordinate system at each moment according to the locator data; wherein the radar matrix calculation module is configured to calculate the radar transformation matrix of the radar in each time period by using the locator transformation matrix as an initial value and obtaining the result of point cloud matching of the radar data by using the normal distribution transformation method and the iterative closest point method.

[0057] According to an embodiment of the present application, the rotation calibration module includes a rotation setting module and a rotation calculation module that are communicatively connected to each other, wherein the rotation setting module is configured to set that the transformation from the locator coordinate system to the radar coordinate system is to rotate successively around the Z axis, the Y axis, and the Z axis; wherein the rotation calculation module is configured to calculate the rotation vectors of rotating around the Z axis and the Y axis successively in the process of transforming from the locator coordinate system to the radar coordinate system according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period through the YZ angle calibration model, so as to obtain the angles of rotating around the Z axis and the Y axis respectively.

[0058] According to an embodiment of the present application, the rotation calibration module includes a rotation setting module and a rotation calculation module that are communicatively connected to each other, wherein the rotation setting module is configured to set that the transformation from the locator coordinate system to the radar coordinate system is to rotate successively around the X axis, the Y axis, and the Z axis; wherein the rotation calculation module is configured to calculate the rotation vectors of rotating around the X axis and the Y axis successively in the process of transforming from the locator coordinate system to the radar coordinate system according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period through the YX angle calibration model, so as to obtain the angles of rotating around the X axis and the Y axis respectively.

[0059] According to an embodiment of the present application, the calibration system of the autonomous mobile platform based on planar motion further includes a parameter optimization module, configured to optimize all the calibrated parameters by using the nonlinear least squares method to obtain the optimal solution of the calibration parameters.

[0060] According to another aspect of the present application, the present application further provides an electronic device, including:

[0061] a processor for executing program instructions; and

[0062] a memory, wherein the memory is configured to store program instructions executable by the processor to implement a calibration method for an autonomous mobile platform based on planar motion, and the calibration method for the autonomous mobile platform based on planar motion includes the steps of:

[0063] S100: Obtain radar data and locator data collected simultaneously by a radar and a locator mounted on the autonomous mobile platform performing planar motion;

[0064] S200: According to the radar data and the locator data, obtain a radar transformation matrix and a locator transformation matrix in each time period, wherein the radar transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the radar in each time period, and the locator transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the locator in each time period;

[0065] S300: According to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period, calibrate the rotation angles of two dimensions in the process of transforming from the locator coordinate system to the radar coordinate system; and

[0066] S400: According to the calibrated rotation angles and the radar transformation matrix and the locator transformation matrix in the same time period, calibrate the remaining parameters of the relative pose between the radar and the locator.

[0067] Through the understanding of the following description and the drawings, further objects and advantages of the present invention will be fully embodied.

[0068] These and other objects, features and advantages of the present invention will be fully embodied by the following detailed description, drawings and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flowchart showing a calibration method for an autonomous mobile platform based on planar motion according to an embodiment of the present invention.

[0070] Figure 2 shows a flowchart of a matrix obtaining step in the calibration method for the autonomous mobile platform based on planar motion according to the above embodiment of the present invention.

[0071] Figure 3 shows a schematic diagram of the transformation relationship between a locator and a radar in the matrix obtaining step according to the above embodiment of the present application.

[0072] Figure 4 Shows a first example of the rotation calibration step in the calibration method of the planar motion-based autonomous mobile platform according to the above embodiments of the present invention.

[0073] Figure 5 Shows a second example of the rotation calibration step in the calibration method of the planar motion-based autonomous mobile platform according to the above embodiments of the present invention.

[0074] Figure 6 Is a block diagram schematic of a calibration system for a planar motion-based autonomous mobile platform according to an embodiment of the present invention.

[0075] Figure 7 Shows a block diagram schematic of an electronic device according to an embodiment of the present invention. Detailed implementation manners

[0076] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and other obvious variations can be thought of by those skilled in the art. The basic principles defined in the following description can be applied to other implementation manners, variant schemes, improvement schemes, equivalent schemes, and other technical schemes that do not depart from the spirit and scope of the present invention.

[0077] In the present invention, the term "a" in the claims and the specification should be understood as "one or more". That is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple. Unless it is clearly indicated in the disclosure of the present invention that the number of the element is only one, the term "a" cannot be understood as being unique or single, and the term "a" cannot be understood as a limitation on the number.

[0078] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0079] At present, although current academic research on radar and locator calibration methods mostly focuses on hand-eye calibration or improved hand-eye calibration solutions, these calibration methods require a large amount of rotation on each coordinate axis (such as the x-axis, y-axis, and z-axis) of the autonomous mobile platform (such as a robot or an intelligent vehicle). This is not realistic for some projects that require the autonomous mobile platform to only perform planar motion (such as a sweeping robot that only moves on a horizontal ground) and cannot be applied. Therefore, to solve the above problems, the present application proposes a calibration method, system, and device for an autonomous mobile platform based on planar motion, which can analyze the motion relationship of sensors (such as radar and locator, etc.) mounted on the autonomous mobile platform during each time period and calibrate the relative pose parameters between the sensors in two steps.

[0080] Specifically, first, analyze the relationship between the radar and the locator during the motion process: divide the motion process into multiple segments according to time, calculate the motion of the radar and the motion of the locator during each time period respectively to list the rotation transformation relationship; according to multiple groups of data, use the least squares method to calculate the two angles of rotation around any two coordinate axes respectively during the process of transforming from the radar coordinate system to the locator coordinate system; and then use these two angles to correct the entire motion process to make the entire motion process become a planar motion in an ideal state, so that the entire calibration task can be transformed into a two-dimensional space for execution. Secondly, analyze the motion relationship between the radar and the locator in the two-dimensional space after angle correction; list the transformation equation according to the pose transformation of the radar and the locator during each time period and considering the errors they carry; calculate the remaining parameters to be calibrated through the least squares method. Finally, to ensure the accuracy of calibration, an overall optimization can be performed on the motion relationship in the three-dimensional space again to obtain the optimal solution of each parameter.

[0081] It should be noted that, different from the existing calibration methods, the calibration method of the autonomous mobile platform based on planar motion in the present application first considers the influence of noise, reduces the divergence caused by instrument errors during the calculation process, and improves the accuracy of the calibration result; secondly, both least squares problems are obtained through analysis and derivation, and there is no need to assign initial values to the parameters to be calibrated; finally, the calibration method of the present application does not require any calibration board, but uses unknown features to analyze the motion of the radar, and the applicable scenarios are more extensive.

[0082] Schematic method

[0083] Referring to Figures 1 to 5 as shown in the accompanying drawings of the specification, a calibration method for an autonomous mobile platform based on planar motion according to an embodiment of the present invention is illustrated. Specifically, as Figure 1 shown, the calibration method for the autonomous mobile platform based on planar motion may include the steps:

[0084] S100: Obtain radar data and locator data collected simultaneously by a radar and a locator mounted on the autonomous mobile platform performing planar motion.

[0085] S200: Obtain a radar transformation matrix and a locator transformation matrix for each time period according to the radar data and the locator data, where the radar transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the radar for each time period, and the locator transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the locator for each time period.

[0086] S300: Calibrate the rotation angles in two dimensions during the process of transforming from the locator coordinate system to the radar coordinate system according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period; and

[0087] S400: Calibrate the remaining parameters of the relative pose between the radar and the locator according to the calibrated rotation angles and the radar transformation matrix and the locator transformation matrix in the same time period.

[0088] It should be noted that since the calibration method of the autonomous mobile platform based on planar motion in this application first calibrates the rotation angles in two dimensions to correct the motion of the autonomous mobile platform into a true planar motion, so that the autonomous mobile platform no longer requires a fixed trajectory and a demanding pose, therefore, the calibration method of the autonomous mobile platform based on planar motion in this application can calibrate an autonomous mobile platform performing simple planar motion, and no reference object and calibration board are required during the calibration process, and it is applicable to any complex scenario.

[0089] More specifically, in step S100 of the calibration method of the autonomous mobile platform based on planar motion: the autonomous mobile platform equipped with the radar and the locator preferably performs planar motion on a horizontal plane, so that the radar and the locator simultaneously collect the corresponding radar data and locator data during the motion. It can be understood that in other examples of this application, the autonomous mobile platform equipped with the radar and the locator can also move on other planes.

[0090] More preferably, the autonomous mobile platform equipped with the radar and the locator preferably performs an S-shaped planar motion on a horizontal plane, so as to simplify the motion process of the autonomous mobile platform while ensuring that the data required for calibration can be collected.

[0091] It should be noted that the radar in this application can be, but is not limited to, implemented as a lidar system for detecting information such as the position and speed of a target; in other words, the radar data in this application can include the point cloud data of the detected target. The locator in this application can be, but is not limited to, implemented as a Beidou navigation system locator or a GPS locator for obtaining the pose, orientation, and speed, etc. of the autonomous mobile platform; in other words, the locator data in this application can include the positioning and orientation information of the autonomous mobile platform.

[0092] In addition, after starting the autonomous mobile platform to perform an S-shaped planar motion on a horizontal plane and collecting the radar data and the locator data, it is necessary to obtain the motion conditions of the radar and the locator in each time period, that is, the radar transformation matrix and the locator transformation matrix in each time period.

[0093] According to the above embodiments of this application, as Figure 2 shown, step S200 of the calibration method for the autonomous mobile platform based on planar motion may include the steps:

[0094] S210: Calculate the locator transformation matrix of the locator in each time period by obtaining the transformation relationship from the locator coordinate system to the world coordinate system at each moment according to the locator data; and

[0095] S220: Take the locator transformation matrix as the initial value, and use the normal distribution transformation method and the iterative closest point method to obtain the result of point cloud matching in the radar data, and calculate the radar transformation matrix of the radar in each time period.

[0096] Exemplarily, in step S210, since the locator data contains the positioning and orientation information of the autonomous mobile platform, this application can obtain the transformation relationship from the locator coordinate system to the world coordinate system at each moment. Denote the transformation relationship at time i as the transformation relationship at time i + 1 as Furthermore, calculate the transformation relationship of the locator from time i to time i + 1 through the time transformation model that is, the locator transformation matrix in the time period from i to i + 1

[0097] Preferably, the time transformation model in this application can be, but is not limited to, implemented as:

[0098]

[0099] In the step S220, the transformation matrix of the radar can be obtained by matching the point cloud data at two moments, and the result of point cloud matching is obtained by the normal distribution transformation method (abbreviated as NDT) and the iterative closest point method (abbreviated as ICP) in sequence. Since both NDT and ICP require good initial values, and both the radar and the locator are fixed on the autonomous mobile platform, the motion transformations of the radar and the locator within the same time period are relatively small. Therefore, in this application, the transformation matrix of the locator is used as the initial value to calculate the transformation relationship of the radar from time i to time i + 1 through the normal distribution transformation method and the iterative closest point method i.e., the transformation matrix of the radar within the time period from i to i + 1

[0100] It should be noted that the transformation matrix of the radar in this application can be decomposed into the rotation transformation relationship (including rotation axis vector, rotation matrix, rotation axis, and rotation angle) and the translation transformation relationship of the radar. For example, use to represent the rotation matrix of the radar from time i to time i + 1, to represent the rotation vector (quaternion) of the radar from time i to time i + 1, represents the angle by which the radar rotates around the rotation axis [k X , k Y , k Z from time i to time i + 1, represents the translation vector of the radar from time i to time i + 1. Similarly, the locator in this application can be decomposed into the rotation transformation relationship (including rotation axis vector, rotation matrix, rotation axis, and rotation angle) and the translation transformation relationship of the locator. For example, use to represent the rotation matrix of the locator from time i to time i + 1, to represent the rotation vector (quaternion) of the locator from time i to time i + 1, represents the angle by which the locator rotates around the rotation axis [k X , k Y , k Z from time i to time i + 1, represents the translation vector of the locator from time i to time i + 1.

[0101] To obtain the transformation relationship between the radar and the locator, in this application, R L,G is used to represent the rotation matrix from the locator to the radar, q L，G is used to represent the rotation vector (such as quaternion) from the locator to the radar, and tL，G Represents the translation vector from the locator to the radar.

[0102] Exemplarily, according to the transformation relationship as Figure 3 shown, it can be known that there are two methods to transform from the locator coordinate system at time i to the radar coordinate system at time i + 1. The first method is that the locator first moves from time i to time i + 1, and then at time i + 1, it is transformed from the locator coordinate system to the radar coordinate system; the second method is to first transform from the locator coordinate to the radar coordinate system at time i, and then move the radar from time i to time i + 1. In particular, since the transformations of the above two methods are substantially equal, the present application can obtain the corresponding rotation transformation relationship and translation transformation relationship as shown in the following formulas (1) and (2):

[0103]

[0104]

[0105] Among them, in the above formula (2), I 3 represents the third-order identity matrix.

[0106] However, in fact, the above transformation relationship is only an ideal situation. In reality, the radar and the locator are usually affected by noise, and the noise is mainly reflected in the data acquired by the sensor, that is, it will cause translation errors and rotation errors, and further make the entire motion process no longer a planar motion in a rational state. To solve the above problems, the present application first assumes that there is noise in the translation matrix where and δq both represent noise; similarly, there is also corresponding noise in the rotation matrix Then assume where u represents the length of the first displacement of the radar, obeys the normal distribution. In summary, when considering the influence of noise, the rotation transformation relationship and translation transformation relationship adopted by the present application are adjusted to the following formulas (3) and (4):

[0107]

[0108]

[0109] Based on this, in the first example of the present application, as Figure 4 shown, the step S300 of the calibration method of the autonomous mobile platform based on planar motion may include the steps:

[0110] S310: Set that the transformation from the locator coordinate system to the radar coordinate system is to rotate successively around the Z-axis, Y-axis, and Z-axis; and

[0111] S320: Calculate the rotation vectors that rotate around the Z-axis and Y-axis successively during the process of transforming from the locator coordinate system to the radar coordinate system through the YZ angle calibration model according to the rotation transformation relationships of the radar and the locator within the same time period, so as to obtain the angles of rotation around the Z-axis and Y-axis respectively.

[0112] Preferably, the YZ angle calibration model of the present application can be but is not limited to being implemented as:

[0113]

[0114] Wherein:

[0115]

[0116] Wherein in the above formula, s represents the sine sin, and c represents the cosine cos; and there are two constraint conditions as follows:

[0117] q YZ1 q YZ4 =q YZ2 q YZ3 , ||q YZ || = 1.

[0118] Wherein q YZ represents the rotation vector that rotates around the Z-axis and Y-axis successively from the locator coordinate system to the radar coordinate system, that is, the quaternion q YZ =[q YZ1 , q YZ2 , q YZ3 , q YZ4 , and correspondingly, the angle of rotation around the Y-axis is β and the angle of rotation around the Z-axis is γ; represents the rotation vector of the radar during the time period from time i to time i + 1; represents the rotation vector of the locator during the time period from time i to time i + 1; represents the angle of rotation of the locator during the time period from time i to time i + 1; represents the angle of rotation of the radar during the time period from time i to time i + 1; represents the rotation vector that minimizes the YZ angle calibration model.

[0119] Exemplarily, for the motion situation with real-world interference, the present application can, by using the rotation vector q YZ , and rotation angles and Input the YZ angle calibration model, and obtain the rotation vector that minimizes the YZ angle calibration model by the least squares method Thus, the angle of rotation about the Y-axis is β and the angle of rotation about the Z-axis is γ when transforming from the locator coordinate system to the radar coordinate system

[0120] It should be noted that the YZ angle calibration model can be, but is not limited to, reasonably simplified and derived from the above formulas (3) and (4). Specifically, according to Trawny's conclusion, when q 1 、q 2 are quaternions, there is a relational expression:

[0121] Among them,

[0122] At this time, the above formula (3) can be changed to the following formula (6):

[0123]

[0124] It can be understood that to calibrate the rotation angle between the radar and the locator, it is necessary to minimize the angle error of the transformation from the locator coordinate system at time i to the radar coordinate system at time i + 1, that is, to minimize the following formula (7):

[0125]

[0126] However, since the calculation of directly minimizing the above formula (7) is too complex, the present application simplifies the above formula (7). Specifically, first assume that q L，G is the rotation vector that rotates about the Z, Y, and Z axes respectively, and the corresponding rotation angles are α, β, γ, then

[0127] The quaternions in the formula are: q z (α) = [0, 0, s(α / 2), c(α / 2)] T ; q z (β) = [0, s(β / 2), 0, c(β / 2)] T ; q z (γ) = [0, 0, s(γ / 2), c(γ / 2)] T .

[0128] Then, η i in the above formula (7) can be rewritten as the following formula (8):

[0129]

[0130] Since and q z (α) are both rotated about the Z-axis, so the two can be swapped to obtain the following equation (9):

[0131]

[0132] Next, combining equations (5) and (9) above gives the following equation (10):

[0133]

[0134] Where:

[0135] After that, denote q YZ =[q YZ1 , q YZ2 , q YZ3 , q YZ4 , is an orthogonal matrix, which can be taken from the above equation (10), and then substituting the above equation (5) into the above equation (10) gives the following equation (11):

[0136]

[0137] Finally, the present application further rewrites the above equation (11) into a different form to obtain the YZ angle calibration model.

[0138] It should be noted that according to the above embodiments of the present application, in the step S400 of the calibration method of the autonomous mobile platform based on planar motion:

[0139] According to the translational transformation relationship of the radar and the translational transformation relationship of the locator within the same time period, the remaining relative pose parameters in the process of transforming from the locator coordinate system to the radar coordinate system are calculated through the translational calibration model.

[0140] Preferably, the translational calibration model of the present application can be but is not limited to being implemented as:

[0141]

[0142]

[0143]

[0144] Where: represents the rotation angle of the locator during the time period from time i to time i + 1; t m represents the first two dimensions of the translation vector of the locator during the time period from time i to time i + 1; α represents the angle of the final rotation about the Z-axis in the transformation from the locator coordinate system to the radar coordinate system; t Xrepresents the translation amount along the X-axis in the transformation from the locator coordinate system to the radar coordinate system; t Y represents the translation amount along the Y-axis in the transformation from the locator coordinate system to the radar coordinate system.

[0145] Exemplarily, after obtaining the rotation vector that minimizes the YZ angle calibration model through the step S300 subsequently, the present application minimizes the translation error from the locator coordinate system at time i to the radar coordinate system at time i + 1, that is, minimizes the above formula (4), to obtain the remaining parameters of the relative pose between the radar and the locator, such as α, t X and t Y and so on.

[0146] Specifically, first substitute the rotation vector into the above formula (4) to obtain the following formula (12):

[0147]

[0148] wherein,[[]] represents the rotation angle of the locator during the time period from time i to time i + 1; represents the translation vector of the radar after angle correction during the time period from time i to time i + 1, denoted as represents the translation vector of the locator during the time period from time i to time i + 1.

[0149] It can be understood that, in the absence of noise interference, all terms in the third row of the above formula (12) should be zero, but in fact they are not, especially which is caused by the noise of the radar. Since the autonomous mobile platform of the present application only performs planar motion, the displacement change in the vertical direction (such as the Z-axis direction) will not be considered, and the third dimension of

[0150]

[0151] represents the translation amount on the Z-axis, so the present application does not consider the translation of the Z-axis. In particular, because the planar motion cannot calibrate the translation amount of the Z-axis, removing the interference of the Z-axis translation can make the calibration result more accurate. Thus, the present application can delete the third row in the above formula (12) to obtain the following formula (13):

[0152]

[0153] Furthermore, by organizing the above formula (13), the following formula (14) can be obtained:Then the above formula (14) can be transformed into the translation calibration model, so that the parameters α and t can be solved through the translation calibration model. X , t Y , u, to obtain the corresponding calibration results: the angle α of the final rotation around the Z axis and the translation amount t along the X axis in the transformation from the locator coordinate system to the radar coordinate system X and the translation amount t along the Y axis Y .

[0154] It is worth mentioning that, in order to ensure the accuracy of the calibration results, the present application can perform an overall optimization on the motion relationship in the three-dimensional space to obtain the optimal solutions of each calibration parameter (including the rotation angles α, β, γ and the translation amounts t X , t Y ) in the transformation from the locator coordinate system to the radar coordinate system.

[0155] Specifically, according to the above embodiments of the present application, as Figure 1 shown, the calibration method of the autonomous mobile platform based on planar motion may further include the steps:

[0156] S500: Optimize all the calibrated parameters through the nonlinear least squares method to obtain the optimal solutions of the calibration parameters.

[0157] Exemplarily, after calibrating the rotation angles α, β, γ and the translation amounts t X , t Y in the transformation from the locator coordinate system to the radar coordinate system, perform the nonlinear least squares method on to optimize the above calibration parameters to obtain the optimal solutions of each parameter.

[0158] In summary, when establishing the angle calibration model and the translation calibration model of the present application, the noises brought by each sensor (the radar and the locator) and the environment are taken into account, greatly reducing the influence of the noises. For example, when there is about 8 cm of noise in both the radar and the locator, the translation offset in the calibration results of the calibration method of the autonomous mobile platform based on planar motion of the present application is less than 1.5 cm, and the angle offset is less than 0.8°, which can fully meet the actual industrial requirements.

[0159] It should be noted that, in the second example of the present application, as Figure 5 shown, the step S300 of the calibration method of the autonomous mobile platform based on planar motion may include the steps:

[0160] S310’: Set the transformation from the locator coordinate system to the radar coordinate system to rotate successively around the X axis, the Y axis, and the Z axis; and

[0161] S320’: Calculate the rotation vectors that rotate around the X-axis and Y-axis successively during the transformation from the locator coordinate system to the radar coordinate system through the YX angle calibration model according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period, so as to obtain the angles of rotation around the X-axis and Y-axis respectively.

[0162] Preferably, the YX angle calibration model of the present application can be implemented but not limited to:

[0163]

[0164] Where:

[0165]

[0166] Where in the above formula, s represents the sine sin, and c represents the cosine cos; and there are two constraint conditions as follows:

[0167] q YX1 q YX4 =q YX2 q YX3 , ||q YX || = 1.

[0168] Where q YX represents the rotation vector that rotates around the X-axis and Y-axis successively from the locator coordinate system to the radar coordinate system, that is, the quaternion q YX = [q YX1 , q YX2 , q YX3 , q YX4 , and correspondingly, the angle of rotation around the Y-axis is β and the angle of rotation around the X-axis is γ; represents the rotation vector of the radar during the time period from time i to time i + 1; represents the rotation vector of the locator during the time period from time i to time i + 1; represents the angle of rotation of the locator during the time period from time i to time i + 1; represents the angle of rotation of the radar during the time period from time i to time i + 1; represents the rotation vector that minimizes the YX angle calibration model.

[0169] Exemplarily, for the motion situation with real interference, the present application can input the rotation vector q YX , and the rotation angles and into the YX angle calibration model, and obtain the rotation vector that minimizes the YX angle calibration model by the least square method Thus, the angle of rotation about the Y-axis is β and the angle of rotation about the X-axis is γ when transforming from the locator coordinate system to the radar coordinate system.

[0170] It should be noted that, according to the second example of the present application, after obtaining the rotation vector that minimizes the YX angle calibration model through the step S300 the present application minimizes the translation error of transforming from the locator coordinate system at time i to the radar coordinate system at time i + 1, that is, minimizes the above formula (4), to obtain the remaining parameters of the relative pose between the radar and the locator, such as α, t X and t Y and so on.

[0171] Specifically, first substitute the rotation vector into the above formula (4) to obtain the following formula (12’):

[0172]

[0173] where represents the rotation angle of the locator during the time period from time i to time i + 1; represents the translation vector of the radar during the time period from time i to time i + 1 after angle correction, denoted as represents the translation vector of the locator during the time period from time i to time i + 1.

[0174] It can be understood that, in the absence of noise interference, all terms in the third line of the above formula (12’) should be zero, but in fact they are not, especially which is caused by the noise of the radar. Since the autonomous mobile platform of the present application only performs planar motion, the displacement change in the vertical direction (such as the Z-axis direction) will not be considered, and the third dimension of represents the translation amount on the Z-axis. Therefore, the present application does not consider the translation of the Z-axis. In particular, because the translation amount of the Z-axis cannot be calibrated for planar motion, removing the interference of the Z-axis translation can make the calibration result more accurate. Thus, the present application can delete the third line in the above formula (12’) to obtain the following formula (13’):

[0175]

[0176] Furthermore, by organizing the above formula (13’), the following formula (14’) can be obtained:

[0177]

[0178] Then the above formula (14’) can still be transformed into the translation calibration model:

[0179]

[0180]

[0181]

[0182] Wherein: represents the rotation angle of the locator during the time period from time i to time i + 1; t m represents the first two dimensions of the translation vector of the locator during the time period from time i to time i + 1; α represents the angle of the last rotation around the Z-axis in the transformation from the locator coordinate system to the radar coordinate system; t X represents the translation amount along the X-axis in the transformation from the locator coordinate system to the radar coordinate system; t Y represents the translation amount along the Y-axis in the transformation from the locator coordinate system to the radar coordinate system.

[0183] Furthermore, the parameter α, t X , t Y , u can be solved through the translation calibration model to obtain the corresponding calibration results: the angle α of the last rotation around the Z-axis in the transformation from the locator coordinate system to the radar coordinate system, the translation amount t along the X-axis X and the translation amount t along the Y-axis Y .

[0184] Schematic system

[0185] Referring to the Figure 6 shown in the accompanying drawings of the specification, a calibration system for an autonomous mobile platform based on planar motion according to an embodiment of the present invention is illustrated, which is used to calibrate a radar and a locator mounted on the autonomous mobile platform. Specifically, as Figure 6As shown, the calibration system 1 of the autonomous mobile platform based on planar motion may include a data acquisition module 10, a matrix acquisition module 20, a rotation calibration module 30, and a translation calibration module 40 that are communicatively connected to each other. The data acquisition module 10 is configured to acquire radar data and locator data collected simultaneously by a radar and a locator mounted on the autonomous mobile platform performing planar motion. The matrix acquisition module 20 is configured to obtain a radar transformation matrix and a locator transformation matrix for each time period based on the radar data and the locator data, where the radar transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the radar for each time period, and the locator transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the locator for each time period. The rotation calibration module 30 is configured to calibrate the rotation angles in two dimensions during the process of transforming from the locator coordinate system to the radar coordinate system based on the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period. The translation calibration module 40 is configured to calibrate the remaining parameters of the relative pose between the radar and the locator based on the calibrated rotation angles and the radar transformation matrix and the locator transformation matrix in the same time period.

[0186] It should be noted that according to the above embodiments of the present application, as Figure 6 shown, the matrix acquisition module 20 may include a locator matrix calculation module 21 and a radar matrix calculation module 22 that are communicatively connected to each other. The locator matrix calculation module 21 is configured to calculate the locator transformation matrix for each time period of the locator by obtaining the transformation relationship from the locator coordinate system to the world coordinate system at each moment based on the locator data. The radar matrix calculation module 22 is configured to calculate the radar transformation matrix for each time period of the radar by using the locator transformation matrix as an initial value and obtaining the result of point cloud matching of the radar data by using the normal distribution transformation method and the iterative closest point method.

[0187] In an example of the present application, as Figure 6 shown, the rotation calibration module 30 may include a rotation setting module 31 and a rotation calculation module 32 that are communicatively connected to each other. The rotation setting module 31 is configured to set that the transformation from the locator coordinate system to the radar coordinate system rotates sequentially around the Z-axis, the Y-axis, and the Z-axis. The rotation calculation module 32 is configured to calculate the rotation vectors rotating around the Z-axis and the Y-axis in sequence during the process of transforming from the locator coordinate system to the radar coordinate system through a YZ angle calibration model based on the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period, so as to obtain the angles of rotation around the Z-axis and the Y-axis respectively.

[0188] Certainly, in another example of the present application, the rotation calibration module 30 may also include a rotation setting module 31 and a rotation calculation module 32 that are communicatively connected to each other, where the rotation setting module 31 is used to set that the transformation from the locator coordinate system to the radar coordinate system is to rotate successively around the X-axis, Y-axis, and Z-axis; where the rotation calculation module 32 is used to calculate, according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator within the same time period, the rotation vectors of the successive rotations around the X-axis and Y-axis during the transformation from the locator coordinate system to the radar coordinate system through the YX angle calibration model, so as to obtain the angles of rotation around the X-axis and Y-axis respectively.

[0189] It is worth mentioning that, according to the above embodiments of the present application, as Figure 6 shown, the calibration system 1 of the autonomous mobile platform based on planar motion may further include a parameter optimization module 50, which is used to optimize all the calibrated parameters by the nonlinear least squares method to obtain the optimal solution of the calibration parameters.

[0190] Schematic electronic device

[0191] Next, with reference to Figure 7 to describe an electronic device according to an embodiment of the present invention ( Figure 7 shows a block diagram of an electronic device according to an embodiment of the present invention). As Figure 7 shown, the electronic device 60 includes one or more processors 61 and a memory 62.

[0192] The processor 61 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 60 to perform desired functions.

[0193] The memory 62 may include one or more computing program products, and the computing program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computing program instructions may be stored on the computer-readable storage media, and the processor 61 may run the program instructions to implement the methods of the various embodiments of the present invention described above and / or other desired functions.

[0194] In one example, as Figure 7 shown, the electronic device 60 may further include: an input device 63 and an output device 64, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0195] For example, the input device 63 can be, for example, a camera module for collecting image data or video data, etc.

[0196] The output device 64 can output various information to the outside, including classification results, etc. The output device 64 can include, for example, a display, a speaker, a printer, and a communication network and remote output devices connected thereto, etc.

[0197] Of course, for the sake of simplicity, Figure 7 only some of the components related to the present invention in the electronic device 60 are shown in [the figure], and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application scenarios, the electronic device 60 may further include any other appropriate components.

[0198] Schematic computing program product

[0199] In addition to the above methods and devices, an embodiment of the present invention may also be a computing program product, which includes computing program instructions that, when run by a processor, cause the processor to execute the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above in this specification.

[0200] The computing program product can be written in any combination of one or more programming languages for the program code to perform the operations of the embodiments of the present invention. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the C language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0201] Furthermore, an embodiment of the present invention may also be a computer-readable storage medium, on which computing program instructions are stored, and the computing program instructions, when run by a processor, cause the processor to execute the steps in the above methods in this specification.

[0202] The computing-readable storage medium may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0203] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are only examples and not limitations. It cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present invention. Additionally, the above-disclosed specific details are only for illustrative and facilitating understanding purposes and are not limitations. The above details do not limit the present invention to necessarily adopt the above specific details for implementation.

[0204] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present invention are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open-ended terms, meaning "including but not limited to", and can be used interchangeably with each other. The word "or" and "and" used herein refer to the word "and / or" and can be used interchangeably with each other, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to" and can be used interchangeably with each other.

[0205] It should also be noted that in the devices, equipment, and methods of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention.

[0206] The above description of the disclosed aspects enables any person skilled in the art to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0207] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and without departing from the said principles, any variations or modifications can be made to the embodiments of the present invention.

Claims

1. Calibration method for an autonomous mobile platform based on planar motion, characterized in that, it includes the steps: S100: Obtain radar data and locator data collected simultaneously by a radar and a locator mounted on the autonomous mobile platform undergoing planar motion; S200: Based on the radar data and the locator data, obtain the radar transformation matrix and the locator transformation matrix for each time period, where the radar transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the radar for each time period, and the locator transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the locator for each time period; S300: Based on the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period, calibrate the rotation angles of two dimensions during the process of transforming from the locator coordinate system to the radar coordinate system; and S400: Based on the calibrated rotation angles and the radar transformation matrix and the locator transformation matrix in the same time period, calibrate the remaining parameters of the relative pose between the radar and the locator; wherein, in the step S400: Based on the translation transformation relationship and the locator translation transformation relationship in the same time period, calculate the remaining relative pose parameters during the process of transforming from the locator coordinate system to the radar coordinate system through a translation calibration model; wherein, the translation calibration model is implemented as: Wherein: represents the rotation angle of the locator during the time period from time i to time i + 1; t m represents the first two dimensions of the translation vector of the locator during the time period from time i to time i + 1; α represents the angle of the last rotation around the Z axis in the transformation from the locator coordinate system to the radar coordinate system; t X represents the translation amount along the X axis in the transformation from the locator coordinate system to the radar coordinate system; t Y represents the translation amount along the Y axis in the transformation from the locator coordinate system to the radar coordinate system; u represents the length of the first displacement of the radar.

2. The calibration method for an autonomous mobile platform based on planar motion according to claim 1, wherein, in the step S100: The autonomous mobile platform equipped with the radar and the locator undergoes an S-shaped planar motion on a horizontal plane.

3. The calibration method for an autonomous mobile platform based on planar motion according to claim 1, wherein, the step S200 includes the steps: Calculate the locator transformation matrix of the locator for each time period by obtaining the transformation relationship from the locator coordinate system to the world coordinate system at each moment based on the locator data; and Taking the locator transformation matrix as the initial value, and using the normal distribution transformation method and the iterative closest point method to obtain the result of point cloud matching of the radar data, calculate the radar transformation matrix of the radar for each time period.

4. The calibration method for an autonomous mobile platform based on planar motion according to claim 1, wherein, the step S300 includes the steps: Set the transformation from the locator coordinate system to the radar coordinate system to be a rotation around the Z-axis, Y-axis, and Z-axis in sequence; and Based on the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period, calculate the rotation vectors of the rotations around the Z-axis and Y-axis in sequence during the process of transforming from the locator coordinate system to the radar coordinate system through a YZ angle calibration model to obtain the angles of rotation around the Z-axis and Y-axis respectively.

5. The calibration method for an autonomous mobile platform based on planar motion according to claim 4, wherein, the YZ angle calibration model is implemented as: ； Wherein: where in the above formula, s represents sine sin, c represents cosine cos; and there are two constraint conditions as follows: q YZ1 q YZ4 = q YZ2 q YZ3 , ||q YZ || = 1; where q YZ represents the rotation vector obtained by successively rotating around the Z-axis and Y-axis from this positioning coordinate system to this radar coordinate system, that is, the quaternion q YZ =[q YZ1 , q YZ2 , q YZ3 , q YZ4 , and correspondingly, the rotation angle around the Y-axis is β and the rotation angle around the Z-axis is γ; represents the rotation vector of this radar during the time period from time i to time i + 1; represents the rotation vector of this locator during the time period from time i to time i + 1; represents the rotation angle of this locator around the rotation axis [k X , k Y , k Z during the time period from time i to time i + 1; represents the rotation angle of this radar around the rotation axis [k X , k Y , k Z during the time period from time i to time i + 1; represents the rotation vector that minimizes this YZ angle calibration model.

6. The calibration method for an autonomous mobile platform based on planar motion according to claim 1, wherein, The step S300 includes the steps of: Setting that the transformation from the locator coordinate system to the radar coordinate system is a rotation in sequence about the X-axis, Y-axis, and Z-axis; and According to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator within the same time period, calculating the rotation vectors for the successive rotations about the X-axis and Y-axis during the transformation from the locator coordinate system to the radar coordinate system through the YX angle calibration model, so as to obtain the angles of rotation about the X-axis and Y-axis respectively.

7. The calibration method for an autonomous mobile platform based on planar motion according to claim 6, wherein, The YX angle calibration model is implemented as: ； Wherein: wherein in the above formula, s represents sine sin, and c represents cosine cos; and there are two constraint conditions as follows: q YX1 q YX4 = q YX2 q YX3 , ||q YX || = 1; where q YX represents the rotation vector obtained by successively rotating the positioning coordinate system about the X-axis and Y-axis to the radar coordinate system, i.e., the quaternion q YX = [q YX1 , q YX2 , q YX3 , q YX4 , and the corresponding rotation angle about the Y-axis is β and the rotation angle about the X-axis is γ; represents the rotation vector of the radar during the time period from time i to time i + 1; represents the rotation vector of the locator during the time period from time i to time i + 1; represents the rotation angle of the locator about the rotation axis [k X , k Y , k Z during the time period from time i to time i + 1; represents the rotation angle of the radar about the rotation axis [k X , k Y , k Z during the time period from time i to time i + 1; represents the rotation vector that minimizes the YX angle calibration model.

8. The calibration method for an autonomous mobile platform based on planar motion according to any one of claims 1 to 7, further comprising the step of: S500: Optimizing all the calibrated parameters through the nonlinear least squares method to obtain the optimal solution of the calibration parameters.

9. A calibration system for an autonomous mobile platform based on planar motion, used for calibrating a radar and a locator mounted on the autonomous mobile platform, characterized in that the calibration system for the autonomous mobile platform based on planar motion includes components communicatively connected to each other: A data acquisition module, configured to acquire radar data and locator data simultaneously collected by the radar and the locator mounted on the autonomous mobile platform performing planar motion; A matrix obtaining module, configured to obtain a radar transformation matrix and a locator transformation matrix for each time period according to the radar data and the locator data, wherein the radar transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the radar for each time period, and the locator transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the locator for each time period; A rotation calibration module, configured to calibrate the rotation angles of two dimensions during the transformation from the locator coordinate system to the radar coordinate system according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period; and A translation calibration module, configured to calibrate the remaining parameters of the relative pose between the radar and the locator according to the calibrated rotation angles and the radar transformation matrix and the locator transformation matrix in the same time period; wherein, the translation calibration module is further configured to: calculate the remaining relative pose parameters during the transformation from the locator coordinate system to the radar coordinate system through the translation calibration model according to the translation transformation relationship and the translation transformation relationship of the locator in the same time period; wherein, the translation calibration model is implemented as: Wherein: represents the rotation angle of the locator during the time period from time i to time i + 1; t m represents the first two dimensions of the translation vector of the locator during the time period from time i to time i + 1; α represents the angle of the last rotation around the Z axis in the transformation from the locator coordinate system to the radar coordinate system; t X represents the translation amount along the X axis in the transformation from the locator coordinate system to the radar coordinate system; t Y represents the translation amount along the Y axis in the transformation from the locator coordinate system to the radar coordinate system; u represents the length of the first displacement of the radar.

10. The calibration system for an autonomous mobile platform based on planar motion according to claim 9, wherein, The matrix acquisition module includes a locator matrix calculation module and a radar matrix calculation module that are communicatively connected to each other. The locator matrix calculation module is configured to calculate the locator transformation matrix of the locator in each time period by obtaining the transformation relationship from the locator coordinate system to the world coordinate system at each moment according to the locator data. The radar matrix calculation module is configured to calculate the radar transformation matrix of the radar in each time period by using the locator transformation matrix as an initial value and obtaining the result of point cloud matching of the radar data by using the normal distribution transformation method and the iterative closest point method.

11. The calibration system of an autonomous mobile platform based on planar motion according to claim 9, wherein, the rotation calibration module includes a rotation setting module and a rotation calculation module that are communicatively connected to each other. The rotation setting module is configured to set that the transformation from the locator coordinate system to the radar coordinate system is to rotate successively around the Z-axis, the Y-axis, and the Z-axis. The rotation calculation module is configured to calculate the rotation vectors of the successive rotations around the Z-axis and the Y-axis during the transformation from the locator coordinate system to the radar coordinate system through the YZ angle calibration model according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period, so as to obtain the angles of rotation around the Z-axis and the Y-axis respectively.

12. The calibration system of an autonomous mobile platform based on planar motion according to claim 9, wherein, the rotation calibration module includes a rotation setting module and a rotation calculation module that are communicatively connected to each other. The rotation setting module is configured to set that the transformation from the locator coordinate system to the radar coordinate system is to rotate successively around the X-axis, the Y-axis, and the Z-axis. The rotation calculation module is configured to calculate the rotation vectors of the successive rotations around the X-axis and the Y-axis during the transformation from the locator coordinate system to the radar coordinate system through the YX angle calibration model according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period, so as to obtain the angles of rotation around the X-axis and the Y-axis respectively.

13. The calibration system of an autonomous mobile platform based on planar motion according to any one of claims 9 to 12, further comprising a parameter optimization module for optimizing all the calibrated parameters by using the nonlinear least squares method to obtain the optimal solution of the calibration parameters.

14. An electronic device, characterized in that, it includes: a processor for executing program instructions; and a memory, wherein the memory is configured to store program instructions executable by the processor to implement the calibration method of an autonomous mobile platform based on planar motion. The calibration method of an autonomous mobile platform based on planar motion includes the steps of: S100: Obtain radar data and locator data collected simultaneously by a radar and a locator mounted on the autonomous mobile platform moving in a planar motion. S200: Obtain the radar transformation matrix and the locator transformation matrix for each time period based on the radar data and the locator data, where the radar transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the radar for each time period, and the locator transformation matrix includes the rotation transformation relationship and the translation transformation relationship of the locator for each time period; S300: Calibrate the rotation angles of two dimensions during the process of transforming from the locator coordinate system to the radar coordinate system according to the rotation transformation relationship of the radar and the rotation transformation relationship of the locator in the same time period; and S400: Calibrate the remaining parameters of the relative pose between the radar and the locator according to the calibrated rotation angles and the radar transformation matrix and the locator transformation matrix in the same time period; wherein, in the step S400: Calculate the remaining relative pose parameters during the process of transforming from the locator coordinate system to the radar coordinate system through a translation calibration model according to the translation transformation relationship and the locator translation transformation relationship in the same time period; wherein, the translation calibration model is implemented as: Wherein: represents the rotation angle of the locator during the time period from time i to time i + 1; t m represents the first two dimensions of the translation vector of the locator during the time period from time i to time i + 1; α represents the angle of the last rotation around the Z-axis in the transformation from the locator coordinate system to the radar coordinate system; t X represents the translation amount along the X-axis in the transformation from the locator coordinate system to the radar coordinate system; t Y represents the translation amount along the Y-axis in the transformation from the locator coordinate system to the radar coordinate system; u represents the length of the first displacement of the radar.

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