Front and rear radar external parameter calibration method and device suitable for differential driving AGV (Automatic Guided Vehicle)

Abstract

The invention relates to a front and back radar external parameter calibration method and device suitable for a differential drive AGV. The front and back radar external parameter calibration method comprises the steps that radar data containing line feature information are acquired in the AGV action process, a first initial transformation matrix is generated, registration processing is conducted on the radar data, and a plurality of transformation matrixes corresponding to different moments are obtained through solving; extracting pose information from the transformation matrix for solving to obtain front radar external parameters; obtaining a global point cloud map according to the pose information corresponding to the front radar, and registering the rear radar data to the global point cloud map for iterative optimization to obtain rear radar external parameters; and outputting the front radar external parameter and the rear radar external parameter. According to the front and rear radar external parameter calibration method and device suitable for the differential drive AGV, the front and rear radar external parameters can be synchronously calibrated, and the multiplexing of the rear radar is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mobile robots, and particularly relates to a front-rear radar external parameter calibration method and device suitable for a differential drive AGV. BACKGROUND

[0002] In the prior art, when calibrating the external parameters of the laser radar of a differential drive AGV (AGV), most of the schemes adopt separate calibration of the front laser radar and the rear laser radar, or directly perform manual external parameter calibration according to mechanical values. In the scheme of separately calibrating the external parameters of the front laser radar and the rear laser radar, the AGV is usually placed in a structured environment, and original laser radar data is obtained through a reference object or a geometric structure set by a person; such a method usually adopts a traditional point-to-point iterative closest point matching algorithm (PP-ICP).

[0003] However, the above two calibration schemes have many limitations. First, separate calibration of the front and rear radars is time-consuming and labor-intensive; separate calibration of the external parameters of the front and rear laser radars not only has low calibration efficiency, but more significantly, it also reduces the consistency of the external parameters of the front and rear laser radars, so that the rear laser radar can only be used as an auxiliary obstacle avoidance laser, and if the front and rear laser radars are used simultaneously for positioning, the positioning accuracy will be reduced. Because of the poor accuracy of the external parameters of the rear radar and the low registration accuracy, the existing system usually only uses the rear radar for obstacle avoidance function, and is not suitable for positioning or mapping tasks. Because the function multiplexing of the rear laser radar is limited, in some large-scale, heavily occluded or highly degraded scenes, a single front radar may not be able to guarantee the stability of positioning, and if the positioning robustness is to be enhanced, a large number of auxiliary facilities such as reflectors need to be added, resulting in complex deployment and rising costs.

[0004] In addition, in order to obtain original laser radar data with obvious features and more accurate matching results, the existing calibration schemes are generally selected to be performed in a structured environment. However, in the registration process of point cloud data, only the Euclidean distance of points is considered, and the line feature information commonly existing in structured environments, such as straight lines and walls, is ignored.

[0005] Therefore, how to improve the consistency of the calibration of the external parameters of the front and rear laser radars under the premise of controlling costs, so that the rear laser radar can fully participate in the positioning and mapping tasks, has become a technical problem that needs to be solved in the field. SUMMARY

[0006] To solve the problems in the prior art, the present application provides a front-rear radar external parameter calibration method and device suitable for a differential drive AGV, which calibrates the front and rear radars simultaneously by fully exploiting and utilizing the line feature information in the environment, so as to ensure the consistency of the external parameters of the front and rear radars.

[0007] According to an embodiment of the present application, in one aspect, a front and rear radar extrinsic parameter calibration method suitable for a differential driving AGV is provided, and the method comprises the following steps: S1: obtaining front radar first laser data containing first line feature information during the AGV performing a first action; generating a first initial transformation matrix according to the front radar mechanical extrinsic parameter and the first action parameter; performing registration processing on the front radar first laser data based on the first line feature information and the first initial transformation matrix, and solving a plurality of first transformation matrices corresponding to different time points; the first transformation matrix contains the pose information of the front radar at the corresponding time point; S2: obtaining front radar second laser data containing second line feature information during the AGV performing a second action; generating a second initial transformation matrix according to the front radar mechanical extrinsic parameter and the second action parameter; performing registration processing on the front radar second laser data based on the second line feature information and the second initial transformation matrix, and solving a plurality of second transformation matrices corresponding to different time points; the second transformation matrix contains the pose information of the front radar at the corresponding time point; S3: obtaining rear radar laser data containing third line feature information during the AGV performing a third action; generating a third initial transformation matrix according to the rear radar mechanical extrinsic parameter and the third action parameter; performing registration processing on the rear radar laser data based on the third line feature information and the third initial transformation matrix, and solving a plurality of third transformation matrices corresponding to different time points; the third transformation matrix contains the pose information of the rear radar at the corresponding time point; S4: extracting the pose information from the first transformation matrix and the second transformation matrix to solve the front radar extrinsic parameter; S5: constructing a global point cloud map according to the corresponding pose information of the front radar; registering the rear radar laser data into the global point cloud map based on the third transformation matrix, and iteratively optimizing the rear radar mechanical extrinsic parameter as the initial value to obtain the rear radar extrinsic parameter; S6: outputting the front radar extrinsic parameter and the rear radar extrinsic parameter.

[0008] Further, in one or more of steps S1-S3: The action parameter comprises speed information corresponding to the action and time difference information between the current frame and the key frame; The initial transformation matrix contains the pose transformation information of the current frame coordinate system relative to the key frame coordinate system; When registering laser data based on line feature information and the corresponding initial transformation matrix, the relative transformation matrix applicable to the current frame coordinate system and the key frame coordinate system is first obtained, and then the transformation matrix between the current frame coordinate system and the fixed reference coordinate system is calculated by combining the key frame transformation matrix and the relative transformation matrix. The fixed reference coordinate system is the corresponding coordinate system of the radar at the start of the operation; The keyframe transformation matrix is ​​used for the transformation between the keyframe coordinate system and the fixed reference coordinate system; The keyframes are updated according to preset threshold conditions.

[0009] Furthermore, in one or more steps S1 to S3, when registering the laser data based on the line feature information and the corresponding initial transformation matrix, the corresponding initial transformation matrix is ​​used as the initial input, and the PL-ICP method is used to match and optimize the laser data of the current frame with the laser data of the key frame to obtain the relative transformation matrix.

[0010] Further, in one or more of steps S1 to S3, the transformation matrix to be optimized is calculated by combining the key frame transformation matrix and the relative transformation matrix. The transformation matrix to be optimized is used as the initial input, and the PL-ICP method is used to match and optimize the laser data of the current frame with the laser data corresponding to the fixed reference coordinate system to obtain the final transformation matrix.

[0011] Further, in step S4, after extracting the pose information from the transformation matrix, an image is generated based on the pose information, the image is fitted, and the front radar extrinsic parameters are calculated based on the fitting results.

[0012] Furthermore: The first action is a rotational action, and the second action is a linear action; The front radar extrinsic parameters include front radar angle extrinsic parameters and front radar coordinate extrinsic parameters; In step S4, after extracting the pose information from the second transformation matrix, an image containing a straight line-like graphic is generated based on the pose information. The fitLine function in the OpenCV library is used to perform straight line fitting on the image to obtain the straight line tilt angle, and the front radar angle extrinsic parameter is calculated based on the straight line tilt angle. After extracting the pose information from the first transformation matrix, an image containing a circular shape is generated based on the pose information. The fitEllipse function in the OpenCV library is used to perform graphic fitting on the image to obtain a rotating rectangle. The center coordinates of the rotating rectangle are extracted, and the front radar coordinate extrinsic parameters are calculated based on the center coordinates and the front radar angle extrinsic parameters.

[0013] Further, The rear radar extrinsic parameters include rear radar angle extrinsic parameters and rear radar coordinate extrinsic parameters. In step S5, the rear radar laser data is registered into the global point cloud map using a PL-ICP method based on a front-rear radar transformation matrix calculated according to the third transformation matrix and the rear radar mechanical extrinsic parameters; when registration is performed, the rear radar angle extrinsic parameters and the rear radar coordinate extrinsic parameters are taken as optimization variables to construct a residual equation, and the rear radar mechanical extrinsic parameters are taken as initial values, and the residual equation is iteratively optimized using a Ceres library to obtain final rear radar extrinsic parameters.

[0014] Further, in step S5, the front radar first laser data and / or the front radar second laser data are converted from a current frame coordinate system to a fixed reference coordinate system based on the first transformation matrix and / or the second transformation matrix, and data points in the front radar first laser data and / or the front radar second laser data are sorted according to angle values in the fixed reference coordinate system, the sorted data points are filtered according to a preset step length, and the global point cloud map is generated according to the filtered data points.

[0015] Further, the third action is a rotating action, and the first action includes the third action.

[0016] In another aspect, the embodiments of the present application provide a front-rear radar extrinsic parameter calibration device suitable for a differential driving AGV, which comprises one or more processors, a memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, for executing the front-rear radar extrinsic parameter calibration method described above.

[0017] The technical solutions provided by the embodiments of the present application can have the following beneficial effects: (a) The front and rear radar extrinsic parameters are simultaneously calibrated by using line features in a calibration environment, the consistency of the front and rear laser radar extrinsic parameters is ensured, the rear radar can participate in positioning, the reuse of the rear radar is improved, and the positioning accuracy is improved; (b) The deployment requirements of the calibration environment are low, time and labor are saved, and the calibration process is efficient; (c) By setting key frames and updating the key frames, the accuracy and convergence speed of the solution are further improved.

[0018] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0020] Figure 1 is a schematic diagram of the steps of the front and rear radar extrinsic parameter calibration method provided by the embodiments of the present application; Figure 2 is a schematic diagram of the steps of obtaining the first transformation matrix in the front and rear radar extrinsic parameter calibration method provided by the embodiments of the present application; Figure 3 is a schematic diagram of the steps of generating a point cloud map in the front and rear radar extrinsic parameter calibration method provided by the embodiments of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0022] The current differential driving AGV is usually equipped with two front and rear laser radars for map construction, autonomous positioning and obstacle avoidance in actual industrial environment. In the prior art, a method of separately calibrating the front radar and the rear radar is generally adopted. This method is low in efficiency and causes poor consistency of the front and rear radar extrinsic parameters, which further causes that the rear radar can only be used in the obstacle avoidance scene, and if the rear radar is used in the positioning scene, the positioning accuracy will be reduced.

[0023] In order to improve the consistency of the front and rear radar calibration and improve the positioning accuracy, the embodiments of the present application propose a front and rear radar extrinsic parameter calibration method suitable for differential driving AGV. Referring to Figure 1 , the front and rear radar extrinsic parameter calibration method comprises the following steps: S1: obtaining front radar first laser data containing first line feature information during the AGV performing a first action; generating a first initial transformation matrix according to the front radar mechanical extrinsic parameters and the first action parameters; performing registration processing on the front radar first laser data based on the first line feature information and the first initial transformation matrix, and solving a plurality of first transformation matrices corresponding to different time points; the first transformation matrix contains the pose information of the front radar at the corresponding time point; S2: During the second action of the AGV, acquire the second laser data of the front radar containing the second line feature information; generate a second initial transformation matrix based on the second action parameters of the front radar mechanical external participation; perform registration processing on the second laser data of the front radar based on the second line feature information and the second initial transformation matrix, and solve for multiple second transformation matrices corresponding to different times; the second transformation matrix contains the pose information of the front radar at the corresponding time. S3: During the third action of the AGV, acquire rear radar laser data containing third line feature information; generate a third initial transformation matrix based on the rear radar mechanical external participation third action parameters; perform registration processing on the rear radar laser data based on the third line feature information and the third initial transformation matrix to obtain multiple third transformation matrices corresponding to different times; the third transformation matrix contains the pose information of the rear radar at the corresponding time. S4: Extract pose information from the first transformation matrix and the second transformation matrix and solve for it to obtain the front radar extrinsic parameters; S5: Based on the pose information corresponding to the front radar, a global point cloud map is constructed; based on the third transformation matrix, the laser data of the rear radar is registered to the global point cloud map, and the mechanical extrinsic parameters of the rear radar are used as initial values ​​for iterative optimization to obtain the extrinsic parameters of the rear radar; S6: Output the front radar extrinsic parameters and the rear radar extrinsic parameters.

[0024] This embodiment utilizes the linear feature information in the environment to obtain the extrinsic parameters of the front radar more conveniently and accurately. Then, based on the linear feature information in the laser data acquired by the front radar, the extrinsic parameters of the rear radar are obtained, realizing the synchronous calibration of the front and rear radars. Since the extrinsic parameters of the front and rear radars are highly consistent, the rear radar can participate in the positioning together, improving the reuse of the rear radar. Under the premise of controlling costs, good positioning accuracy can still be achieved, and the calibration efficiency is improved.

[0025] The above transformation matrices represent the transformation from the radar's current frame coordinate system to a fixed reference coordinate system. The fixed reference coordinate system can be the coordinate system corresponding to the radar at the start of the action or before the start, or it can be a preset input fixed coordinate system.

[0026] The mechanical extrinsic parameters of the front and rear radars are pre-inputted or preset parameters, typically related to the mechanical mounting structure of the corresponding radar. These mechanical extrinsic parameters can reflect the radar's mounting pose to some extent, but their accuracy is limited; in some cases, they can only provide a rough approximation of the pose, or even be almost unrelated to the actual pose. The front and rear radar mechanical extrinsic parameters are mainly used as initial reference values ​​in the iterative solution process, participating in the generation of the starting point for iterative optimization; while the front and rear radar extrinsic parameters finally obtained through iterative optimization can accurately characterize the actual mounting pose of the corresponding radar.

[0027] Referring to Figure 1 and Figure 2 In an embodiment of the present application, in steps S1-S3, to prevent too few overlapping parts of the objects scanned by the two frames of laser light when matching, or too large initial relative pose error obtained by solving, a key frame is introduced as a transition, that is, the transformation from the current frame coordinate system to the key frame coordinate system is solved first, and then the transformation matrix is finally obtained according to the information of the key frame. Specifically, the action parameter includes speed information corresponding to the action and time difference information between the current frame and the key frame; the initial transformation matrix contains the pose transformation information of the current frame coordinate system relative to the key frame coordinate system; when the laser data is registered based on the line feature information and the corresponding initial transformation matrix, a relative transformation matrix applicable to the current frame coordinate system and the key frame coordinate system is obtained first, and then the transformation matrix between the current frame coordinate system and the fixed reference coordinate system is calculated by combining the key frame transformation matrix and the relative transformation matrix; the key frame transformation matrix is used for the transformation between the key frame coordinate system and the fixed reference coordinate system; the key frame is updated according to a preset threshold condition, such as frame number counting and screening, or time difference information screening, or the displacement of the current frame relative to the fixed reference coordinate system can also be referred to.

[0028] Obviously, in the case of the fixed reference coordinate system corresponding to the coordinate system of the corresponding radar at the start of the action, the frame corresponding to this coordinate system is the first key frame, referring to Figure 2 .

[0029] Referring to Figure 1 and Figure 2 In an embodiment of the present application, in steps S1-S3, when the laser data is registered based on the line feature information and the corresponding initial transformation matrix, the corresponding initial transformation matrix is used as the initial input, the current frame laser data and the key frame laser data are matched and optimized by using the point-to-line iterative closest point (PL-ICP) matching method, and the relative transformation matrix is obtained, and then the required transformation matrix is directly calculated from the relative transformation matrix and the key frame transformation matrix.

[0030] Preferably, after the transformation matrix is calculated by combining the key frame transformation matrix and the relative transformation matrix, the transformation matrix is used as the initial input, the current frame laser data and the laser data corresponding to the fixed reference coordinate system are further matched and optimized by using the PL-ICP method, and the final output transformation matrix is obtained.

[0031] The PL-ICP method uses a piecewise linear method to approximate the actual surface represented by the laser data, and uses the distance from the target frame point to the line connecting the nearest two points in the reference frame to construct an error function. Compared with the PP-ICP method, the error function of the PL-ICP method is not only close to the actual situation, but also has higher accuracy and faster convergence speed. The iterative calculation steps of the PL-ICP method are as follows: 1. Convert the target frame data to the reference frame (i.e. the key frame) coordinate system according to the given initial transformation matrix; 2. For each data point in the target frame, find the two nearest points in the reference frame in the Euclidean distance; 3. Remove points with larger errors; 4. Use the point-to-line distance to construct a minimum error equation; 5. Continuously iterate and update the transformation matrix; By using the PL-ICP method twice, the final output matrix can have excellent accuracy.

[0032] The PL-ICP method is sensitive to the initial value, and the result of the iterative calculation depends on the accuracy of the initial value. In this embodiment, the first, second and third initial transformation matrices and the first, second and third transformation matrices are used as the initial input of the PL-ICP method, which can efficiently obtain accurate calculation results.

[0033] In the embodiment of the application, a mixed calculation method of PL-ICP and PP-ICP can also be used, or other methods using environmental line features for point cloud data registration.

[0034] In an embodiment of the application, the first action is a rotation action, and the second action is a translation action. Obviously, it can also be the opposite, that is, the first action is a translation action, and the second action is a rotation action, and the remaining operations can be adjusted accordingly. For the sake of convenience, the first action is a rotation action and the second action is a translation action in the following description. The first action includes a third action, that is, the third action is the same as the first action, or the third action is a part of the rotation of the first action (generally at least half of it to ensure that the front laser point cloud map can cover the entire calibration environment). The calibration of the rear radar is almost synchronized with the calibration of the front radar, or the front radar is calibrated for a period of time, and then the front and rear radars are calibrated synchronously. Since the above actions all occur in the same environment, the environmental line features corresponding to the first line feature information, the second line feature information and the third line feature information often have overlapping parts, and in some cases they can be completely the same.

[0035] The technical solutions of the present application are displayed and explained in combination with a specific embodiment. It should be noted that the technical features in the specific embodiment should not limit the protection scope of the present application, especially the specific size of the calibration environment, the specific data of frame number screening, etc., which are only used as examples to help understanding.

[0036] In the calibration process of the specific embodiment, the motion mode of rotating one circle in place and then moving straight for about 50 cm is adopted. When fitting and solving the laser radar external parameter, the rotation center is regarded as the vehicle body center, so there is a certain requirement for the differential drive of the AGV. The straight distance can be determined according to actual needs. If more rich data amount is needed when moving straight, the value can be appropriately increased. If not, the value can be appropriately reduced. The value of the distance should not constitute a limitation on the protection scope of the present application.

[0037] The calibration method is to use the PL-ICP method for matching. In order to ensure the accuracy of the PL-ICP matching, there are certain requirements for the calibration environment used. The requirements are as follows: Calibration environment: AGV must be in a structured environment with appropriate size. Selecting an appropriate size environment can ensure that the AGV will not collide with objects during calibration, and can ensure the accuracy of the original laser radar point cloud data ranging (for laser radar, there is a certain error when ranging. When the ranging is small or far, it will cause greater error). Selecting a structured environment is to obtain more regular laser radar point cloud, which provides better original data for the PL-ICP method. In this embodiment, the calibration environment used for testing is generally a closed "rectangle" surrounded by flat boards or walls. The length and width of the rectangle is about 3-4 times the length of the AGV vehicle body, and the height of the rectangle is about 2 times the height of the laser radar.

[0038] Differential drive structure: before calibration, it is necessary to ensure that the AGV can stably move straight, and can stably rotate in place with the vehicle body center as the center. That is, the motion mode of rotating one circle in place and then moving straight for about 50 cm is adopted in the calibration process. Therefore, it is necessary to ensure that the AGV can stably move straight, and can stably rotate in place with the vehicle body center as the center. Therefore, the method of this embodiment is only applicable to the AGV with differential drive.

[0039] The solved front laser radar extrinsic parameter is the transformation of the front laser radar coordinate system (denoted as front_laser) relative to the vehicle body center coordinate system (denoted as base), and the rear laser radar extrinsic parameter is the transformation of the rear laser radar coordinate system (denoted as back_laser) relative to front_laser. For the front laser radar data in the calibration process, the relative poses of the front laser radar data in rotation and straight running relative to the respective reference coordinate systems are solved by using the point-to-line iterative closest point (PL-ICP) method. In the rotation action, the rotation angle between the current frame and the key frame is constructed by using the angular velocity and the inter-frame time difference, and the initial transformation matrix is generated in combination with the mechanical extrinsic parameter; in the straight running action, the translation amount is generated based on the linear velocity, the inter-frame time difference and the relative position of the front radar and the AGV center, and the initial transformation matrix is constructed.

[0040] Referring to Figure 2 Preferably, in order to ensure the coincidence degree of the laser scanning area in the matching, a key frame selection strategy is set in the embodiment: when the rotation angle of the current frame relative to the key frame exceeds a set threshold (for example, 5°), the current frame is updated as a new key frame.

[0041] Preferably, when the PL-ICP method is used for registration, the point-to-line matching is first solved between the current frame laser data and the key frame laser data, to obtain the relative transformation matrix of the current frame coordinate system relative to the key frame coordinate system; then, the final transformation matrix of the current frame coordinate system relative to the fixed reference coordinate system is calculated in combination with the transformation relationship of the key frame relative to the fixed reference coordinate system.

[0042] Only the rear laser radar data in the rotation process is processed, and the processing manner is consistent with that of the front laser radar data in the rotation. Still taking front_laser as the fixed reference coordinate system, all the front laser radar data in the rotation process is converted into this coordinate system, and the point cloud data is filtered, so that a laser radar point cloud map with front_laser as the global coordinate system can be obtained. The transformation of back_laser relative to front_laser is taken as an optimization variable, the PL-ICP method is used to match and solve the rear laser radar data and the point cloud map, so as to construct a nonlinear optimization, and the result of the nonlinear optimization is the rear laser radar extrinsic parameter.

[0043] The straight line fitting is performed on all the relative pose point sets of the front laser radar obtained in the straight running process, and the obtained tilt angle is the rotation angle corresponding to the rotation matrix in the transformation matrix from the base coordinate system to the front_laser coordinate system. The rotation rectangle is solved for all the relative pose point sets of the front laser radar obtained in the rotation process, and the center coordinate value of the rotation rectangle is the translation corresponding to the transformation matrix from the base coordinate system to the front_laser coordinate system. The transformation matrix is inverted to obtain the transformation matrix from the front_laser to the base coordinate system, and the front laser radar external parameter is obtained.

[0044] More specifically, referring to Figure 2 , for the front laser radar data in the rotation process, the coordinate system corresponding to the front laser radar at the beginning of the rotation is taken as the fixed reference coordinate system, that is, the front_laser coordinate system. The relative pose of the front laser radar coordinate system (denoted as front_i) at other time relative to the front_laser is solved by using the PL-ICP method. In order to ensure the matching accuracy of the PL-ICP, the initial relative pose is solved by means of the front laser coordinate system mechanical external parameter, the AGV rotation speed and the laser frame time difference. In order to prevent the matching of the two frames of laser scanned objects from being too small and the initial relative pose solved by using the rotation speed from being too large, the key frame is constantly updated as the reference frame when solving the PL-ICP, and the coordinate system corresponding to the key frame is denoted as front_key. The initial relative pose transformation matrix relative to the front_key is constructed as follows: , In the above formula, is the angular velocity of the rotation in place, and the AGV generally rotates counterclockwise, that is , is the time difference of the two frames of laser radar data, is the rotation angle value in place, is the mechanical value of the Euclidean distance from the front_laser coordinate system to the base coordinate system, represents the initial transformation matrix of the front laser i-th coordinate system front_i to the key frame coordinate system front_key. As the initial value when solving the matching result of the first frame of laser data and the key frame laser data , the solving result of the PL-ICP is denoted as , which represents the transformation matrix from the front_i coordinate system to the front_key coordinate system. Since the key frame is selected from the laser frame, it is ​​The values are known, and the transformation from front_i to front_laser is calculated according to the following formula: , In the above formula represents the initial transformation from front_i to front_laser, and the final transformation from front_i to front_laser should be theoretically calculated by the above formula, but the present embodiment considers the existence of matching errors between front_key and front_laser and matching errors between front_i and front_key, and therefore defines the result calculated by the above formula as a new initial transformation. The is taken as an initial value, and the PL-ICP is used to calculate The matching result of the laser data corresponding to the front_laser coordinate system is recorded as . All and the corresponding laser data including are recorded.

[0045] For the rotation process, an angle threshold is defined to screen the key frames, and the angle threshold selected in the present embodiment is 5°, that is, when the relative transformation matrix of a coordinate system with respect to the key frame coordinate system corresponds to an angle greater than 5°, the key frame is updated. The scheme of taking other angles as the angle threshold (such as 3°, 6°, and 10°) to update the key frame and improve the matching precision is also within the protection scope of the present technical scheme.

[0046] For the front laser radar data in the straight running process, the processing idea is basically the same as that in the rotation process, and only the initial transformation matrix with respect to the key frame coordinate system front_key and the screening method of the key frame are different. For the convenience of understanding, variables with similar meanings are named similarly or identically as in the rotation process, and the following formula is the construction method of the initial relative pose transformation matrix with respect to front_key: ; In the above formula is the time difference between two frames of laser radar data, represents the AGV straight running speed, represents the distance of the AGV straight running, represents the mechanical value of the front laser radar angle parameter, which is generally 0, still represents the front laser ​The initial transformation matrix from the coordinate system front_j to the keyframe coordinate system front_key is still represented by front_laser, which represents the fixed reference coordinate system of the front lidar during the straight-line process.

[0047] For the straight-line process, a distance threshold is defined to filter keyframes. The distance threshold selected in the embodiments of this application is... The threshold is 0.05m, meaning that when the straight-line distance corresponding to the relative transformation matrix of a coordinate system relative to the keyframe coordinate system is greater than 0.05m, the keyframe is updated. In actual calibration, the distance threshold is fine-tuned (e.g., 0.1m, 0.08m; for a straight-line distance of approximately 50cm, the distance threshold range is generally between 0.02m and 0.1m. A value that is too small may lead to a larger relative error, while a value that is too large may lead to insufficient data. For different straight-line distances, the range of the distance threshold will also fluctuate accordingly to balance error control and sufficient data volume) to update keyframes and improve matching accuracy. This approach is also within the protection scope of this invention.

[0048] At this point, the records include All transformation matrices including .

[0049] For the rear LiDAR data during rotation, the coordinate system corresponding to the rear LiDAR at the start of rotation is used as a fixed reference coordinate system, namely the back_laser coordinate system. The PL-ICP method is used to match and solve for the relative pose of the rear LiDAR coordinate system (denoted as back_i) relative to back_laser at other times. To ensure the matching accuracy of PL-ICP, the initial relative pose is solved using the Euclidean distance mechanical value of back_laser relative to the base coordinate system, the AGV rotation speed, and the laser frame time difference. To prevent insufficient overlap of the object scanned by two laser frames during matching and to avoid large errors in the initial relative pose obtained using rotation speed, keyframes are continuously updated as reference frames during PL-ICP solving; the coordinate system corresponding to the keyframe is denoted as back_key.

[0050] The processing method for rear lidar data during rotation is similar to that for front lidar data during rotation, and will not be elaborated here. To represent the first step in the rotation process The relative pose of the laser data after each frame relative to the fixed reference coordinate system back_laser. Records include... All of them And the corresponding laser data.

[0051] In one embodiment of the present application, after the pose information is extracted from the transformation matrix in step S4, an image is generated according to the pose information, the image is graphically fitted, and the front radar external parameter is calculated according to the fitting result. The front radar external parameter includes a front radar angle external parameter and a front radar coordinate external parameter.

[0052] Specifically, in one specific embodiment, after the pose information is extracted from the second transformation matrix in step S4, an image containing a straight line-like graph is generated according to the pose information, a straight line fitting is performed on the image using the fitLine function in the OpenCV library to obtain a straight line tilt angle, and the front radar angle external parameter is calculated according to the straight line tilt angle. After the pose information is extracted from the first transformation matrix, an image containing a circle-like graph is generated according to the pose information, a graph fitting is performed on the image using the fitEllipse function in the OpenCV library to obtain a rotated rectangle, the center coordinates of the rotated rectangle are extracted, and the front radar coordinate external parameter is calculated according to the center coordinates and the front radar angle external parameter.

[0053] That is, in the actual process of solving the front radar external parameter, the pose information is extracted from the plurality of transformation matrices obtained from the above two actions. Based on the pose translation extracted in the second action (straight line), a point cloud trajectory is drawn in the OpenCV image and a straight line fitting is performed using the fitLine function, so as to solve the front radar angle external parameter; based on the pose translation extracted in the first action (rotation), a trajectory ring-like graph is drawn, an ellipse center is fitted using the fitEllipse function, and then the front radar coordinate external parameter is calculated.

[0054] Still in combination with the above specific embodiment, the front laser radar external parameter is solved, and is denoted as , the angle external parameter is solved first , and then the coordinate external parameter is solved . With the aid of OpenCV, a three-channel white image matrix with the same width and height is created. The corresponding translation (denoted as ) in all matrices is taken out, all the translations are drawn into the image matrix with a resolution of 0.005 and an offset value of half of the width. When , the is a unit matrix, so the first point on the image matrix is at the center of the image matrix. The image matrix used in the embodiments of the present application is a white image matrix with a width and a height of 1000. The data points in the image matrix are close to a straight line, and a straight line fitting is performed on the data points in the image matrix using the fitLine function in the OpenCV library, and the tilt angle of the straight line is calculated according to the fitting result, denoted as . Since all the are calculated in the fixed reference coordinate system of the front laser, i.e., the tilt angle is calculated in the fixed reference coordinate system of the front laser, i.e., the tilt angle is calculated in the fixed reference coordinate system of the front laser, i.e., the tilt angle is calculated in the fixed reference coordinate system of the front laser, i.e., the tilt angle is calculated in the fixed reference coordinate system of the front laser, i.e., the tilt angle .

[0055] Similarly, a three-channel white image matrix with the same width and height is created by means of OpenCV. The corresponding translation (denoted as ) in all matrices is extracted, and all the translation amounts are drawn into the image matrix with a resolution of 0.005 and an offset of half the width. When is the unit matrix, the first point on the image matrix is at the center of the image matrix. In the image matrix, all data points will form a figure close to a circle. By means of the fitEllipse function in the OpenCV library, the data points are fitted, and a rotation rectangle calculated according to the least square method is returned. Because the differential driving AGV rotates around the center of its vehicle body (i.e., the origin of the base coordinate system), the center of the rotation matrix can be considered as the origin of the base coordinate system, and the center of the rotation rectangle is denoted as . Similar to the solution of the above , all are calculated in the fixed reference coordinate system of the front laser, so the rotation rectangle is also taken as the reference coordinate system of the front laser, and represents the translation component from the base coordinate system to the front laser coordinate. The front laser extrinsic parameter can be solved according to the following formula: . .

[0056] In the above formula, represents the transformation matrix from the base coordinate system to the front laser coordinate system, and its inverse is , function extracts from . The right side of the above formula is the step of solving the extrinsic parameter obtained by the formula on the left side.

[0057] At this point, the extrinsic parameter of the front laser coordinate system is obtained. ​

[0058] Referring to Figure 3 In an embodiment of the present application, the rear radar extrinsic parameters include rear radar angle extrinsic parameters and rear radar coordinate extrinsic parameters; in step S5, the rear radar laser data is registered into the global point cloud map using a PL-ICP method based on a front-rear radar transformation matrix calculated according to the third transformation matrix and the rear radar mechanical extrinsic parameters; when registration is performed, the rear radar angle extrinsic parameters and the rear radar coordinate extrinsic parameters are taken as optimization variables to construct a residual equation, and the rear radar mechanical extrinsic parameters are taken as initial values, and the residual equation is iteratively optimized using a Ceres library to obtain final rear radar extrinsic parameters.

[0059] In an embodiment of the present application, in step S5, the first transformation matrix is used to convert the front radar laser data from a current frame coordinate system to a fixed reference coordinate system, and the data points in the front radar laser data are sorted according to angle values in the fixed reference coordinate system, the sorted data points are filtered according to a preset step length, and the global point cloud map is generated according to the filtered data points.

[0060] That is, in the process of solving the rear radar extrinsic parameters, first, all the rear radar laser data is converted to the front_laser coordinate system according to the front radar extrinsic parameters, and a point cloud map corresponding to the front radar data is constructed. In this embodiment, the front radar laser data is preferably filtered by angle as an index, and a high-resolution point cloud map is finally generated.

[0061] Subsequently, the initial transformation relationship of the current frame rear radar coordinate system relative to the front radar fixed reference coordinate system is estimated based on the combination of the rear radar mechanical extrinsic parameters and the third transformation matrix. Taking this as the initial value, a residual function between the rear radar data and the point cloud map is constructed, and the rear radar extrinsic parameters (angle and position) are taken as optimization variables, and the Ceres nonlinear optimization library is used for iterative optimization to finally solve the high-precision rear radar extrinsic parameters.

[0062] Finally, the final extrinsic parameters of the front radar and the rear radar are output.

[0063] In an embodiment, the rear radar mechanical extrinsic parameters can be regarded as a set containing multiple types of parameters, which can be described with the rear radar initial state as the reference system or with the front radar as the reference system. In actual application, the mechanical extrinsic parameters in the above two reference systems can be approximately equivalent under certain conditions, so that the mechanical extrinsic parameters in the set can be used without distinction for extrinsic parameter solving when there is a requirement for calculation speed or implementation simplicity. However, when there is a high demand for precision and robustness, the mechanical extrinsic parameters in different reference systems can be distinguished to achieve more accurate pose estimation and extrinsic parameter optimization.

[0064] Still in combination with the above specific embodiments, the external parameter of the back laser radar coordinate system is solved and denoted as The reference coordinate system of the back laser radar coordinate system back_laser is the front laser radar coordinate system front_laser. First, a point cloud map with the front laser radar coordinate system front_laser as the global coordinate system is constructed, as shown in Figure 3 The steps are as follows: According to the obtained The corresponding front laser data is converted into the front_laser coordinate system.

[0065] According to the obtained coordinate values in the front_laser, the distance value and the angle are calculated and recorded. Assuming that the coordinate value of a point in the front_laser is , then the distance value and the angle value can be calculated as follows: , In the above formula, function represents the signed angle between the line connecting the origin to the point and the positive axis, function represents the solution of the arithmetic square root.

[0066] (3) The laser points are reordered according to the angle value from small to large.

[0067] (4) All laser points are screened with a fixed angle step. A suitable angle value smaller than the laser resolution value is selected as the screening step , and the embodiment of the present application selects 0.002 radian. The screening steps are as follows: the first laser point is retained, and the angle value is denoted as . It is judged whether the angle value of the second laser point is greater than . If it is greater, the second laser point is retained, and the angle value is assigned to . Otherwise, the second laser point is discarded, and it is judged whether the third laser point meets the requirements, and so on.

[0068] (5) Thus, a point cloud map with an angle resolution of is obtained, and the point cloud map is denoted as .

[0069] The transformation matrix recorded in the rotation process and the transformation matrix corresponding to the mechanical value (denoted as ) of the external parameter of the back laser radar (relative to the front laser radar coordinate system) are multiplied, and all the corresponding back laser radar data (denoted as ) is transformed into the front_laser coordinate system. The back_laser extrinsic parameter As the variable to be optimized, As the initial value of the variable to be optimized, the back_laser data under the front_laser and the point cloud map are matched by PL-ICP to construct the residual equation, and the residual equation is optimized by using the nonlinear optimization library Ceres of Google. The result obtained by iterative optimization of the Ceres library is the back_laser extrinsic parameter The solving process of the back_laser extrinsic parameter can be represented by the following formula: , In the above formula, represents the transformation matrix of the back_laser coordinate system to the front_laser coordinate system, and the mechanical value is used to give the initial value of the transformation matrix, represents the transformation matrix of the back_laser coordinate system to the front_laser coordinate system, represents the transformation of the back_laser data of the frame to the front_laser coordinate system by means of the transformation matrix The function represents the PL-ICP matching of the laser data and the point cloud map, The function represents that the back_laser data is taken as the variable to be optimized, and the residual function constructed by using the PL-ICP matching of the frame laser data and the point cloud map is optimized and solved, represents the total number of laser data frames. The function will automatically assign the result of the solution to the variable .

[0070] At this point, the front_laser coordinate system extrinsic parameter and the back_laser coordinate system extrinsic parameter are obtained.

[0071] In an embodiment of the present application, a front and rear radar extrinsic parameter calibration device suitable for differential driving AGV is provided, which comprises one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs are used to execute the above-mentioned front and rear radar extrinsic parameter calibration method.

[0072] Embodiments of the present application utilize a point cloud matching method in simultaneous localization and mapping (SLAM) technology, and a nonlinear optimization approach to simultaneously calibrate the external parameters of the 2D front and rear laser radars of a differential drive AGV, which can greatly reduce the time required for calibration while ensuring consistency and improving the accuracy of the external parameters of the front and rear laser radars.

[0073] This technology can be mainly applied to the following aspects.

[0074] Most differential drive AGVs use a scheme of separately calibrating the external parameters of the front and rear laser radars, or directly manually calibrate the external parameters according to mechanical values. The above two calibration schemes not only consume time and effort, but also reduce the consistency of the external parameters of the front and rear laser radars, causing the rear laser radar to only be used as an auxiliary obstacle avoidance laser. If the front and rear laser radars are used for positioning, the positioning accuracy will be reduced. In order to obtain feature obvious original laser radar data and further obtain more accurate matching results, existing calibration schemes generally choose to be performed in a structured environment. Existing calibration schemes for the external parameters of the front and rear laser radars of differential drive AGVs generally separately calibrate the external parameters of the front and rear laser radars in a structured environment, and use PP-ICP in the matching processing of laser radar data, which faces the problems of low calibration efficiency, poor consistency of the external parameters of the front and rear laser radars, and poor reusability of the rear laser radar. When facing some special environments (such as large area scenes or highly degenerated scenes), it is difficult to guarantee the positioning accuracy using only the front laser radar, at which time more reflective plates and other measures are often selected to help positioning. However, this type of scheme is very dependent on the deployment and modification of the site.

[0075] The external parameters of the rear laser radar calibrated by the existing scheme are poor in consistency. In order to improve the accuracy of laser radar data matching, existing schemes basically choose to place the AGV in a structured environment constructed by humans (such as a rectangular environment constructed using straight boards, walls, etc.), to obtain original laser radar data with obvious straight line features. However, the line features of the structured environment are not fully utilized in the implementation of the existing scheme, and the PL-ICP method with faster convergence speed and higher accuracy in structured environments is not used.

[0076] Embodiments of the present application can synchronize the external parameters of the front and rear laser radars of the differential drive AGV, reduce the time consumption of calibration, ensure the consistency of the external parameters of the front and rear laser radars, and improve the multiplexing of the laser. Embodiments of the present application further combine the characteristics of the structured environment, use the PL-ICP method with an initial value that has higher accuracy in a structured environment for matching, fully utilize the line features in the environment, and construct an error function that can better reflect the actual situation. In the same environment, embodiments of the present application can obtain more accurate external parameters of the laser radar.

[0077] Embodiments of the present application relate to the following technologies: SLAM point cloud matching technology, nonlinear optimization technology, information data processing technology, sensor technology, and the like.

[0078] The method proposed by embodiments of the present application is mainly to improve the consistency of the external parameters of the front and rear laser radars of the differential drive AGV, improve the calibration accuracy, and improve the calibration efficiency. In the daily operation of the AGV, the rear laser radar can not only be used as an auxiliary obstacle avoidance laser radar, but also be used as a positioning laser radar in some extreme scenarios, thereby improving the multiplexing of the sensor, ensuring the positioning accuracy, and reducing the on-site modification cost.

[0079] For example, when the AGV faces a large-scale scene or a highly degraded scene, it is difficult to ensure the positioning accuracy by relying only on the front laser radar, and even the positioning may be lost. In view of the above situation, a solution of adding a reflector plate to the front laser radar (the rear laser radar is still used as an auxiliary obstacle avoidance laser radar) is often adopted, but because the scanning range of a single laser radar is limited, a larger number and higher density of reflector plates need to be deployed at this time, so this solution is very dependent on the deployment and modification of the site.

[0080] In view of the above situation, the positioning solution of the present application embodiments uses the front and rear dual laser radars for synchronous calibration, which can achieve a scanning range of 360°, thereby ensuring the positioning accuracy of the AGV. If it is necessary to further ensure the positioning accuracy, a solution of adding a reflector plate to the dual laser radars can be adopted, in which case only a small number of reflector plates need to be deployed, saving time and effort.

[0081] The technical solution of the present application uses the PL-ICP method to solve the relative pose, and combines the motion characteristics of the differential drive AGV to estimate the initial pose by using the motion speed and time difference, thereby providing an excellent initial estimate for the PL-ICP. For a structured calibration environment, the PL-ICP matching method fully utilizes the line features contained in the laser points, the error function constructed is more consistent with the actual situation, and the solving accuracy is higher. In addition, the traditional PP-ICP algorithm with point feature constraints only has a first-order convergence characteristic, and the convergence speed and calculation efficiency are limited, while the PL-ICP algorithm based on line feature constraints introduces a second-order convergence mechanism, which significantly improves the iteration efficiency and convergence speed.

[0082] In the rotation and straight movement process, the coordinate system corresponding to the initial frame of the front radar is taken as a fixed reference coordinate system, and by combining the mechanical external parameters and the movement parameters (such as the rotation angular velocity, the time difference and the straight movement velocity, etc.), an initial transformation matrix is constructed as the initial value of the PL-ICP matching, so that the initial estimation rationality and the matching precision of the registration are effectively improved.

[0083] In the process of solving the external parameters of the front radar, the translation and rotation in the transformation matrix are extracted to generate a straight line image and a circle image, respectively, and the angle external parameters and the coordinate external parameters of the front radar are fitted by using the fitLine and fitEllipse functions in OpenCV, so that the characteristics of the circle center offset and the angle offset of the differential driving AGV when rotating in place in the front radar coordinate system are completely reproduced, and the external parameter solving is more physically interpretable and stable.

[0084] In the process of solving the external parameters of the rear radar, the laser data of the rear radar is converted to the reference coordinate system of the front radar, and the front radar point cloud map is taken as the reference, a residual function is constructed by using the PL-ICP method, and the mechanical external parameters of the rear radar are taken as the initial value, and iterative optimization is performed in the Ceres nonlinear optimization library, so that the angle and coordinate external parameters of the rear radar are finally solved. The above process realizes the automatic joint calibration of the front and rear radars in the unified reference coordinate system, reduces the calibration time, and ensures the consistency of the external parameters of the front and rear radars and the efficient reuse of the laser.

[0085] In summary, the present application can simultaneously calibrate the external parameters of the front and rear radars, ensure the consistency of the external parameters, greatly shorten the calibration time, support the joint positioning of the double laser radars, improve the system positioning accuracy and the sensor reuse rate, and reduce the field modification and maintenance cost, which has a positive significance for improving the flexibility and reliability of the AGV application.

[0086] In the several embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other manners. For example, the device embodiment described above is only illustrative, and for example, the division of the modules is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the indirect coupling or communication connection between the modules or components can be electrical, mechanical or in other forms.

[0087] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and some or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0088] In addition, each of the functional modules in each of the embodiments of the present application can be integrated in one processing unit, or each of the modules can exist physically, or two or more of the modules can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a hardware plus software function unit.

[0089] It should be noted that, in this document, relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another, without necessarily requiring or implying any actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

Claims

1. A method for calibrating front and rear radar extrinsic parameters of a differential drive AGV, characterized in that, The front-rear radar external parameter calibration method comprises the following steps: S1: In the process of the AGV performing a first action, first laser data of a front radar containing first line feature information is acquired; a first initial transformation matrix is generated according to the mechanical external parameter of the front radar and the first action parameter; the first laser data of the front radar is registered based on the first line feature information and the first initial transformation matrix, and a plurality of first transformation matrices corresponding to different time points are solved; the first transformation matrix contains the pose information of the front radar at the corresponding time point; S2: In the process of the AGV performing a second action, second laser data of the front radar containing second line feature information is acquired; a second initial transformation matrix is generated according to the mechanical external parameter of the front radar and the second action parameter; the second laser data of the front radar is registered based on the second line feature information and the second initial transformation matrix, and a plurality of second transformation matrices corresponding to different time points are solved; the second transformation matrix contains the pose information of the front radar at the corresponding time point; S3: In the process of the AGV performing a third action, laser data of a rear radar containing third line feature information is acquired; a third initial transformation matrix is generated according to the mechanical external parameter of the rear radar and the third action parameter; the laser data of the rear radar is registered based on the third line feature information and the third initial transformation matrix, and a plurality of third transformation matrices corresponding to different time points are solved; the third transformation matrix contains the pose information of the rear radar at the corresponding time point; S4: The pose information is extracted from the first transformation matrix and the second transformation matrix to solve the external parameter of the front radar; S5: The global point cloud map is constructed according to the corresponding pose information of the front radar; the laser data of the rear radar is registered into the global point cloud map based on the third transformation matrix, and the mechanical external parameter of the rear radar is used as the initial value for iterative optimization to obtain the external parameter of the rear radar; S6: The external parameter of the front radar and the external parameter of the rear radar are output.

2. The front-rear radar extrinsic calibration method of claim 1, wherein, In one or more of steps S1-S3: The action parameter comprises speed information corresponding to the action and time difference information between the current frame and the key frame; The initial transformation matrix contains the pose transformation information of the current frame coordinate system relative to the key frame coordinate system; When the laser data is registered based on the line feature information and the corresponding initial transformation matrix, a relative transformation matrix suitable for the current frame coordinate system and the key frame coordinate system is first obtained, and then the transformation matrix between the current frame coordinate system and the fixed reference coordinate system is calculated in combination with the key frame transformation matrix and the relative transformation matrix; The fixed reference coordinate system is the corresponding coordinate system of the corresponding radar at the beginning of the action; The key frame transformation matrix is used for transformation between the key frame coordinate system and the fixed reference coordinate system; The key frame is updated according to a preset threshold condition.

3. The front-rear radar extrinsic calibration method of claim 2, wherein, In one or more of steps S1-S3, when the laser data is registered based on the line feature information and the corresponding initial transformation matrix, the corresponding initial transformation matrix is used as an initial input to match and optimize the current frame laser data and the key frame laser data using the PL-ICP method to obtain the relative transformation matrix.

4. The front-rear radar extrinsic calibration method of claim 3, wherein, In one or more of steps S1-S3, the transformation matrix to be optimized is calculated based on the key frame transformation matrix and the relative transformation matrix, and the transformation matrix to be optimized is used as an initial input to match and optimize the current frame laser data and the laser data corresponding to the fixed reference coordinate system using the PL-ICP method to obtain the final transformation matrix.

5. The front-rear radar extrinsic calibration method of claim 1, wherein, In step S4, after extracting the pose information from the transformation matrix, an image is generated based on the pose information, the image is graphically fitted, and the front radar external parameter is calculated based on the fitting result.

6. The front-rear radar external parameter calibration method of claim 5, wherein: the first motion is a rotation motion, and the second motion is a translation motion; the front radar external parameter includes a front radar angle external parameter and a front radar coordinate external parameter; In step S4, after extracting the pose information from the second transformation matrix, an image containing a straight line-like graph is generated based on the pose information, a straight line fitting is performed on the image using a fitLine function in an OpenCV library to obtain a straight line tilt angle, and the front radar angle external parameter is calculated based on the straight line tilt angle; After extracting the pose information from the first transformation matrix, an image containing a circle-like graph is generated based on the pose information, a graph fitting is performed on the image using a fitEllipse function in the OpenCV library to obtain a rotated rectangle, a center coordinate of the rotated rectangle is extracted, and the front radar coordinate external parameter is calculated based on the center coordinate and the front radar angle external parameter.

7. The front-rear radar external parameter calibration method of claim 1, wherein: the rear radar external parameter includes a rear radar angle external parameter and a rear radar coordinate external parameter; In step S5, based on the front-rear radar transformation matrix calculated based on the third transformation matrix and the rear radar mechanical external parameter, the rear radar laser data is registered into the global point cloud map using the PL-ICP method; when registering, the rear radar angle external parameter and the rear radar coordinate external parameter are used as optimization variables to construct a residual equation, and the rear radar mechanical external parameter is used as an initial value to iteratively optimize the residual equation using a Ceres library to obtain the final rear radar external parameter.

8. The front-rear radar extrinsic calibration method of claim 2, wherein, In step S5, based on the first transformation matrix and / or the second transformation matrix, the front radar first laser data and / or the front radar second laser data are converted from the current frame coordinate system to the fixed reference coordinate system, the data points in the front radar first laser data and / or the front radar second laser data are sorted according to the angle values in the fixed reference coordinate system, the sorted data points are filtered according to a preset step size, and the global point cloud map is generated based on the filtered data points.

9. The front-rear radar extrinsic calibration method of claim 6, wherein, The third motion is a rotating motion, and the first motion includes the third motion.

10. A front and rear radar extrinsic calibration device suitable for a differential drive AGV, characterized in that, The front-rear radar extrinsic parameter calibration device includes one or more processors, a memory, and one or more programs stored in the memory and configured to be executed by the one or more processors to perform the front-rear radar extrinsic parameter calibration method of any one of claims 1 to 9.

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