External Parameter Calibration Method and Device for Integrated Navigation Equipment and LiDAR

Abstract

The present invention provides a method and device for calibrating the external parameters of a combined navigation device and a lidar. The method and device for calibrating the external parameters of the combined navigation device and the lidar provided by the present invention can effectively improve the calculation efficiency and result accuracy of the optimal external parameter calibration solution by obtaining relatively ideal initial external parameter values. The pose information of the lidar is estimated by means of pose interpolation, which is more convenient and efficient than the method of estimating the lidar pose by running the SLAM algorithm. Furthermore, the time consumption of the entire external parameter calibration process is reduced, making the external parameter calibration process more efficient and the external parameter calibration result more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent driving, and in particular, to a method and device for calibrating the external parameters of a combined navigation device and a lidar. Background Art

[0002] High-precision combined navigation devices and lidars are commonly used sensors in the high-precision positioning and perception technology of working machinery. In the actual application process, it is necessary to obtain the pose conversion relationship between the combined navigation device and the lidar. However, it is very difficult to obtain the external parameter calibration parameters only based on the installation positions of these two sensors on the working machinery by using external parameter measurement means.

[0003] Currently, the external parameter calibration of a combined navigation device and a lidar is mainly achieved by converting the data collected by the lidar into the coordinate system of the combined navigation device. On the one hand, external sensors can be used directly for external parameter measurement. However, due to the different installation positions of the combined navigation device and the lidar, the direct measurement process is complex and the measurement error is large.

[0004] On the other hand, the mileage information of the lidar can also be obtained by running the SLAM algorithm with the lidar, and then combined with the mileage information calculated by the combined navigation device itself, the transformation matrix between the two coordinate systems can be calculated to obtain the external parameter calibration result between the two sensors. However, the above method requires a large amount of calculation, takes a long time, and the accuracy of the external parameter calibration result is low. Summary of the Invention

[0005] The present invention provides a method and device for calibrating the external parameters of a combined navigation device and a lidar, so as to solve the defects that the existing methods for calibrating the external parameters of a combined navigation device and a lidar are cumbersome in operation, time-consuming, and low in result accuracy, and to achieve efficient and accurate calibration of the external parameters of a combined navigation device and a lidar.

[0006] In a first aspect, the present invention provides a method for calibrating the external parameters of a combined navigation device and a lidar, the method comprising:

[0007] Obtaining the original pose data collected by the combined navigation device and the original point cloud data collected by the lidar respectively;

[0008] Synchronizing the time of the original point cloud data and the original pose data to obtain the time stamp information after time synchronization;

[0009] Performing pose interpolation on the original pose data according to the time stamp information after time synchronization and the original point cloud data to obtain a pose interpolation result;

[0010] Calculating an initial external parameter value according to the original pose data, the original point cloud data, and the pose interpolation result;

[0011] Based on the initial value of the extrinsic parameter and the pre-constructed optimization objective function, an optimal extrinsic parameter calibration solution is obtained.

[0012] According to an external parameter calibration method of a combined navigation device and a laser radar provided by the present invention, according to the time stamp information after the time synchronization and the original point cloud data, the original posture data is subjected to posture interpolation to obtain a posture interpolation result, including:

[0013] Respectively obtain the posture transformation matrix from the current moment to the initial moment and the corresponding timestamp information of each posture point in the original posture data, and obtain the virtual point corresponding to each radar point in the coordinate system of the integrated navigation device at the corresponding moment;

[0014] Acquire, from the original pose data, adjacent pose points adjacent to the timestamp corresponding to each radar point in the original point cloud data according to the time stamp information after the time synchronization;

[0015] Based on the timestamp information of the adjacent pose points and the corresponding pose transformation matrix, a pose transformation matrix from the virtual point to the initial time point of the integrated navigation device is calculated as a pose interpolation result.

[0016] According to a method for calibrating external parameters of a combined navigation device and a laser radar provided by the present invention, an initial value of an external parameter is calculated based on the original posture data, the original point cloud data and the posture interpolation result, including:

[0017] Selecting a plurality of pose points from the original pose data, and selecting a radar point corresponding to each of the pose points from the original point cloud data;

[0018] Substitute each pose point and the corresponding radar point into the pre-constructed transformation point coordinate expression to obtain multiple equations to be solved with the extrinsic parameter matrix as the unknown quantity, and combine the multiple equations to be solved to calculate the initial value of the extrinsic parameter.

[0019] According to a method for calibrating external parameters of a combined navigation device and a laser radar provided by the present invention, the transformation point coordinate expression is used to describe that the transformation point coordinates are calculated from the pose transformation matrix from the virtual point to the initial time point of the combined navigation device, the pre-constructed external parameter matrix to be determined, and the radar point coordinates.

[0020] According to a method for calibrating external parameters of a combined navigation device and a laser radar provided by the present invention, based on the initial value of the external parameter and a pre-constructed optimization objective function, an optimal external parameter calibration solution is obtained, including:

[0021] Based on the initial external parameters, an optimization objective function is constructed with the minimum sum of distances of the nearest points in the original point cloud data as the optimization objective, and the optimal external parameter calibration solution is obtained by solving.

[0022] According to an external parameter calibration method for a combined navigation device and a lidar provided by the present invention, based on the initial external parameters, an optimization objective function is constructed with the minimum sum of distances of the nearest points in the original point cloud data as the optimization objective, and the optimal external parameter calibration solution is obtained by solving, including:

[0023] Based on the initial external parameters, assign values to the external parameter matrix to be solved;

[0024] According to the assigned external parameter matrix to be solved and the transformed point coordinate expression, obtain the transformed points corresponding to each radar point in the original point cloud data in the coordinate system of the initial moment of the combined navigation device, and obtain a set of transformed points;

[0025] Respectively obtain at least two nearest neighboring transformed points corresponding to each transformed point in the set of transformed points, and calculate the distances between each transformed point and its corresponding at least two neighboring transformed points;

[0026] Sum up the distances between each transformed point and its corresponding at least two neighboring transformed points to obtain the sum of distances of the nearest points;

[0027] Construct an optimization objective function with the minimum sum of distances of the nearest points as the optimization objective, and perform continuous assignment iteration for optimization;

[0028] Until the sum of distances of the nearest points is less than a preset distance threshold or the number of assignment iterations reaches a preset number threshold, take the currently assigned external parameter matrix to be solved as the optimal external parameter calibration solution.

[0029] According to an external parameter calibration method for a combined navigation device and a lidar provided by the present invention, based on the initial external parameters, an optimization objective function is constructed with the minimum sum of distances of the nearest points in the original point cloud data as the optimization objective, and the optimal external parameter calibration solution is obtained by solving, including:

[0030] Based on the initial external parameters, assign values to the external parameter matrix to be solved;

[0031] According to the assigned external parameter matrix to be solved and the transformed point coordinate expression, obtain the transformed points corresponding to each radar point in the current scan frame of the original point cloud data in the coordinate system of the initial moment of the combined navigation device as the first transformed points;

[0032] Determine the radar point with the shortest distance for each radar point in the current scan frame in the next scan frame, and obtain the transformed point corresponding to the radar point with the shortest distance in the coordinate system of the initial moment of the combined navigation device as the second transformed points;

[0033] Take the first transformation point and the second transformation point as the closest distance point pair, and combine all the closest distance point pairs in the original point cloud data into a set of closest distance point pairs;

[0034] Calculate the sum of the distances of all the closest distance point pairs in the set of closest distance point pairs to obtain the sum of the distances of the closest distance points;

[0035] Construct an optimization objective function with the minimum sum of the distances of the closest distance points as the optimization objective, and perform iterative optimization by continuous assignment;

[0036] Until the sum of the distances of the closest distance points is less than a preset distance threshold or the number of assignment iterations reaches a preset number threshold, take the current assigned external parameter matrix to be solved as the optimal external parameter calibration solution.

[0037] In a second aspect, an external parameter calibration device for a combined navigation device and a lidar includes:

[0038] An acquisition module for respectively acquiring the original pose data collected by the combined navigation device and the original point cloud data collected by the lidar;

[0039] A first processing module for synchronizing the time of the original point cloud data and the original pose data to obtain the timestamp information after time synchronization;

[0040] A second processing module for performing pose interpolation on the original pose data according to the timestamp information after time synchronization and the original point cloud data to obtain a pose interpolation result;

[0041] A third processing module for calculating an initial value of the external parameter according to the original pose data, the original point cloud data, and the pose interpolation result;

[0042] A fourth processing module for solving the optimal external parameter calibration solution based on the initial value of the external parameter and a pre-constructed optimization objective function.

[0043] In a third aspect, the present invention further provides a working machine that uses the external parameter calibration method for a combined navigation device and a lidar according to any one of the above.

[0044] In a fourth aspect, the present invention further provides an external parameter calibration system for a combined navigation device and a lidar, the system includes:

[0045] A lidar to be calibrated for collecting the original point cloud data of the target object;

[0046] A combined navigation device to be calibrated for collecting the original pose data of the target object;

[0047] A controller is configured to respectively obtain the original pose data and the original point cloud data; synchronize the time of the original point cloud data and the original pose data to obtain timestamp information after time synchronization; perform pose interpolation on the original pose data according to the timestamp information after time synchronization and the original point cloud data to obtain a pose interpolation result; calculate an initial value of the external parameters based on the original pose data, the original point cloud data, and the pose interpolation result; and solve for an optimal external parameter calibration solution based on the initial value of the external parameters and a pre-constructed optimization objective function.

[0048] The method and device for calibrating the external parameters of the integrated navigation device and the lidar provided by the present invention can effectively improve the efficiency and accuracy of obtaining the optimal external parameter calibration solution by obtaining an ideal initial value of the external parameters. The pose information of the lidar is estimated by means of pose interpolation, which is more convenient and efficient than estimating the lidar pose by running the SLAM algorithm. Furthermore, the time consumption of the entire external parameter calibration process is reduced, making the external parameter calibration process more efficient and the external parameter calibration result more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 is a schematic flowchart of the method for calibrating the external parameters of the integrated navigation device and the lidar provided by the present invention;

[0051] Figure 2 is a schematic flowchart of the calculation process of the initial value of the external parameters;

[0052] Figure 3 is a schematic structural diagram of the device for calibrating the external parameters of the integrated navigation device and the lidar provided by the present invention;

[0053] Figure 4 is a schematic hardware architecture diagram of the external parameter calibration system built during the implementation process;

[0054] Figure 5 is a schematic flowchart of the working process of the external parameter calibration system;

[0055] Figure 6 is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Apparently, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0057] Figure 1 The following shows an external parameter calibration method for a combined navigation device and a lidar provided by an embodiment of the present invention. The method includes:

[0058] Step 110: Obtain the original pose data collected by the combined navigation device and the original point cloud data collected by the lidar respectively.

[0059] In this embodiment, the combined navigation device mainly targets the RTK / IMU combined navigation device. RTK (Real Time Kinematic) is a satellite navigation technology used to improve the accuracy of position data obtained from a satellite-based positioning system. In addition to the information contained in the signal, RTK also uses the measurement of the carrier signal phase and relies on a single reference station or an interpolated virtual station for real-time correction to provide positioning accuracy up to the centimeter level.

[0060] IMU (Inertial Measurement Unit) is a sensor mainly used to detect and measure acceleration and rotational motion.

[0061] Therefore, the RTK / IMU combined navigation device can simultaneously obtain satellite navigation data and inertial measurement data, providing reliable data support for the subsequent external parameter calibration process.

[0062] In this embodiment, the lidar mainly targets the 3D lidar. The 3D lidar can obtain three-dimensional depth information and point cloud data, and has the advantages of high resolution and low power consumption. It is a relatively common perception device used on construction machinery.

[0063] Step 120: Synchronize the original point cloud data and the original pose data in terms of time to obtain the timestamp information after time synchronization.

[0064] In the time synchronization step, first align the original point cloud data and the original pose data in terms of time to achieve time difference compensation for the transmission delay between the lidar and the combined navigation device. In this embodiment, the time difference to be compensated is set as Δt1.

[0065] Then, based on the time difference Δt1 to be compensated, the time difference Δt2 between the initial moment of the lidar and the initial moment of the combined navigation device, and the timestamp t of each scan frame h, the timestamp t of each point in each scan frame from the start time of the scan frame where the point is located of , and finally calculate the timestamp t of each point in each scan frame relative to the initial time of the integrated navigation device p , and the calculation formula is as follows:

[0066] t p = Δt1 + Δt2 + t h + t of (1)

[0067] It is not difficult to see that in this embodiment, the above time synchronization process mainly calculates and updates the timestamp of the lidar. The purpose is to synchronize the timestamp of the lidar with the timestamp of the integrated navigation device as much as possible to ensure the effective progress of the subsequent data processing process.

[0068] Step 130: According to the timestamp information after time synchronization and the original point cloud data, perform pose interpolation on the original pose data to obtain a pose interpolation result.

[0069] In an exemplary embodiment, the process of performing pose interpolation on the original pose data according to the timestamp information after time synchronization and the original point cloud data to obtain a pose interpolation result specifically includes:

[0070] The first step: respectively obtain the pose transformation matrix from the current time to the initial time of each pose point in the original pose data and the corresponding timestamp information, and obtain the virtual point corresponding to each radar point in the coordinate system of the integrated navigation device at the corresponding time.

[0071] In this embodiment, specifically, the pose points of the integrated navigation device are subjected to integral transformation, and through IMU integral transformation, the pose transformation matrix T_t0_t from the current time t k to the initial time t0 is calculated. k The virtual point corresponding to the radar point in the coordinate system of the integrated navigation device at the corresponding time can be obtained by multiplying the coordinates of the radar point by the preset external parameter matrix to be solved.

[0072] The second step: according to the timestamp information after time synchronization, obtain the adjacent pose points adjacent to the timestamp corresponding to each radar point in the original point cloud data from the original pose data.

[0073] Assume that after time synchronization, the timestamp of a certain radar point p i in the original point cloud data collected by the lidar is t p , and the two timestamps closest to the timestamp t p in the integrated navigation device are t k and t k+1 and satisfy t k ≤ t p < t k+1, accordingly, t can be obtained k and t k+1 The pose points corresponding to the moments, that is, adjacent pose points. At the same time, t k The pose transformation matrix T_t0_t from the pose point corresponding to the moment to the initial moment can be obtained k , and t k+1 The pose transformation matrix T_t0_t from the pose point corresponding to the moment to the initial moment k+1 .

[0074] Step 3: Based on the timestamp information of adjacent pose points and the corresponding pose transformation matrices, calculate the pose transformation matrix from the virtual point to the initial moment point of the integrated navigation device as the pose interpolation result.

[0075] Taking the content assumed in the second step above as an example, in this step, the pose transformation matrix from the virtual point to the initial moment point of the integrated navigation device, that is, the virtual pose transformation matrix T_t0_t p The calculation formula is as follows:[[]]

[0076]

[0077] It can be understood that taking the above assumed content as an example, the virtual point mentioned in this embodiment refers to the point corresponding to t p at the moment. Since this point does not actually exist in the integrated navigation coordinate system, this point is called a virtual point.

[0078] Step 140: Calculate the initial value of the external parameters based on the original pose data, the original point cloud data, and the pose interpolation result.

[0079] To implement the external parameter calibration of the integrated navigation device and the lidar, a to-be-solved external parameter matrix is pre-constructed in this embodiment. The to-be-solved external parameter matrix includes rotation angles and translation amounts.

[0080] The to-be-solved external parameter matrix can specifically include three rotation angles and three translation amounts. For example, the rotation angles can be the heading angle α, the roll angle β, and the yaw angle γ, and the translation amounts can be the translation amount b1 on the X coordinate axis, the translation amount b2 on the Y coordinate axis, and the translation amount b3 on the Z coordinate axis. In this way, the to-be-solved external parameter matrix T can be expressed as:[[]]

[0081]

[0082] Among them, R represents the rotation matrix, t represents the translation matrix, a1 to a9 represent the elements in the rotation matrix, and b1 to b3 represent the elements in the translation matrix.

[0083] Therefore, the to-be-solved external parameter matrix T contains six unknowns. By solving the above six unknowns, the solution result of the to-be-solved external parameter matrix can be obtained, and thus the external parameter calibration solution can be obtained.

[0084] In this way, based on the above pose interpolation result, the pre-constructed to-be-solved external parameter matrix, and the radar point coordinates, a transformed point coordinate expression can be constructed. This transformed point coordinate expression is used to describe that the transformed point coordinates are obtained by multiplying the pose transformation matrix from the virtual point to the initial moment point of the integrated navigation device, the to-be-solved external parameter matrix, and the radar point coordinates.

[0085] It can be understood that since the pose transformation matrix of each radar point to the coordinate system of the integrated navigation device at the initial moment can be obtained by left-multiplying the corresponding virtual pose transformation matrix by the to-be-solved external parameter matrix, therefore, when calculating the coordinates of the transformed point corresponding to each radar point in the coordinate system of the integrated navigation device at the initial moment, it can be obtained by left-multiplying the radar point coordinates by its pose transformation matrix to the coordinate system of the integrated navigation device at the initial moment.

[0086] Specifically, taking the coordinate expression of the above radar point p i and the transformed point q i in the coordinate system of the integrated navigation device at the initial moment as an example for illustration, that is:

[0087]

[0088] Among them, represents the pose transformation matrix of the lidar point p i in the coordinate system of the integrated navigation device at the initial moment; T·p i represents the virtual point coordinates obtained by converting the lidar point p i to the coordinate system of the integrated navigation device at the current moment; T_t0_t p ·T·p i represents the transformed point coordinates obtained by converting the lidar point p i to the coordinate system of the integrated navigation device at the initial moment.

[0089] See Appendix Figure 2 , according to the original pose data, the original point cloud data, and the pose interpolation result, the process of calculating the initial value of the external parameter specifically includes:

[0090] First, select multiple pose points from the original pose data, and select the radar points corresponding to each pose point from the original point cloud data.

[0091] After completing time synchronization and pose interpolation, this embodiment extracts five pose points q1, q2, q3, q4, and q5 whose spatial position distances are respectively greater than d1, d2, d3, d4, and d5 from the original pose data collected by the integrated navigation device. Based on the known initial pose point q0, six known pose points can be obtained, namely: Figure 2 Steps 210 and 220 are shown;

[0092] On the other hand, five radar points p1, p2, p3, p4, and p5 corresponding to the five pose points q1, q2, q3, q4, and q5 are extracted from the original point cloud data collected by the lidar. Based on the known initial radar point p0, six known radar points can be obtained, namely Figure 2 Steps 230 and 240 are shown.

[0093] Then, each pose point and the corresponding radar point are substituted into the transformation point coordinate expression to obtain multiple equations to be solved with the extrinsic parameter matrix as the unknown quantity. The multiple equations to be solved are combined to calculate the initial value of the extrinsic parameter.

[0094] See attached Figure 2 In step 250, based on the six known radar points {p0, p1, p2, p3, p4, p5}, the six known pose points {q0, q1, q2, q3, q4, q5} and the above-mentioned transformation point coordinate expressions, six equations with the extrinsic parameter matrix to be determined as unknowns can be constructed. The six unknowns in the extrinsic parameter matrix to be determined can be solved by combining the six equations, thereby obtaining a solution to the extrinsic parameter matrix to be determined, and the solution will be used as the initial extrinsic parameter T0.

[0095] Step 150: Based on the initial value of the extrinsic parameter and the pre-constructed optimization objective function, an optimal extrinsic parameter calibration solution is obtained.

[0096] Specifically, this embodiment constructs an optimization objective function based on the initial value of the extrinsic parameter and takes the sum of the distances of the closest points in the original point cloud data as the optimization goal, and then solves and obtains the optimal extrinsic parameter calibration solution.

[0097] In order to obtain the optimal external parameter calibration solution, this embodiment gradually optimizes the initial value of the external parameter by using the nonlinear optimization method, and finally obtains the optimal external parameter calibration solution.

[0098] In an exemplary embodiment, the process of constructing an optimization objective function and solving the optimal external parameter calibration solution can be achieved in the following two ways:

[0099] The first method is to obtain the closest distance point of each radar point in the current frame in the original point cloud data in the next frame, construct multiple closest distance point pairs, and then calculate the distance of each closest distance point pair. Finally, the sum of the distances of the closest distance points can be obtained.

[0100] Specifically, the method for solving the optimal extrinsic parameter calibration solution specifically includes:

[0101] First, based on the initial value of the extrinsic parameters, assign values to the extrinsic parameter matrix to be solved.

[0102] Then, according to the assigned extrinsic parameter matrix to be solved and the transformation point coordinate expression, obtain the transformation points corresponding to each radar point in the current scan frame of the original point cloud data in the coordinate system at the initial moment of the integrated navigation device, as the first transformation points.

[0103] Determine the radar point with the shortest distance to each radar point in the current scan frame in the next scan frame, and obtain the transformation point corresponding to the radar point with the shortest distance in the coordinate system at the initial moment of the integrated navigation device, as the second transformation points.

[0104] Next, take the first transformation points and the second transformation points as the nearest distance point pairs, and combine all the nearest distance point pairs in the original point cloud data into a set of nearest distance point pairs.

[0105] After that, calculate the sum of the distances of all the nearest distance point pairs in the set of nearest distance point pairs to obtain the sum of the distances of the nearest distance points.

[0106] Then, taking the minimum sum of the distances of the nearest distance points as the optimization objective, construct an optimization objective function, and perform iterative optimization through continuous value assignment.

[0107] Finally, until the sum of the distances of the nearest distance points is less than the preset distance threshold or the number of value assignment iterations reaches the preset number threshold, take the currently assigned extrinsic parameter matrix to be solved as the optimal extrinsic parameter calibration solution.

[0108] In this method, it is necessary to calculate the radar point with the shortest distance to each radar point in each scan frame of the original point cloud data collected by the lidar in the next scan frame, and use the pre-assigned extrinsic parameter matrix to be solved and the transformation point coordinate expression to respectively obtain the transformation points of each radar point and its nearest distance point in the coordinate system at the initial moment of the integrated navigation device. Denote the transformation point corresponding to the current radar point and the transformation point corresponding to its nearest distance point as the nearest distance point pair, and combine all the nearest distance point pairs in the original point cloud data to construct a set of nearest distance point pairs.

[0109] Subsequently, by calculating the sum of the distances of all the nearest distance point pairs in the set of nearest distance point pairs, the sum of the distances of the nearest distance points in the original point cloud data can be obtained, which can be specifically expressed as:

[0110]

[0111] Where x i , y i , z iDenote the coordinates of the \(i\)-th point in the point set \(\sum P\) obtained by rotating the radar points to the coordinate system at the initial time \(t_0\) of the integrated navigation device as \(x'\) i , \(y'\) i , \(z'\) i which are the coordinates of the point in the point set \(\sum P\) that is closest to this point.

[0112] Taking the sum \(D\) of the distances to the closest points as the optimization objective, the following optimization objective function can be constructed, that is:[[]]END]] error Minimize \(D\), and the optimization objective function can be constructed as follows:

[0113]

[0114] where \(n\) represents the number of points in the point set.

[0115] Use the non-linear optimization algorithm for continuous iteration and optimization until \(D\) after a certain iteration error is less than the set distance threshold \(D\) th or when the current iteration number is greater than the maximum iteration number (i.e., the preset number threshold), stop the iteration. At this time, \(T\) is the optimal external parameter calibration solution.

[0116] The second method: The transformed points corresponding to each radar point in the original point cloud data can be obtained, a set of transformed points can be constructed, then the two closest points among the points in the set of transformed points can be found, and the distances from each point in the set to its two closest points (which may include the current point itself) can be calculated. Finally, the sum of the distances from each point to its two closest points can be obtained, which is the sum of the distances to the closest points.

[0117] In the actual application process, the specific process is as follows:

[0118] First, use the initial value \(T_0\) of the external parameters to assign values to the external parameter matrix to be solved;

[0119] Then, according to the assigned external parameter matrix to be solved, the time stamps after time synchronization, and the transformed point coordinate expression, each radar point in the original point cloud data collected by the lidar is transformed to the coordinate system of the integrated navigation device at the initial time \(t_0\), that is, the transformed points corresponding to each radar point in the original point cloud data in the coordinate system of the integrated navigation device at the initial time are obtained, and a set of transformed points is obtained.

[0120] Next, at least two closest neighboring transformed points corresponding to each transformed point are obtained from the set of transformed points, and the distances between each transformed point and its corresponding at least two neighboring transformed points are calculated; in this embodiment, among the two neighboring transformed points corresponding to each transformed point, one may be the point itself, and the other is the actual closest point. In the process of calculating the distance, if one of the neighboring transformed points is the point itself, the distance between the two points is zero, which does not affect the solution of the closest distance.

[0121] After that, sum the distances between each transformation point and at least two adjacent transformation points corresponding thereto to obtain the sum of the distances of the nearest distance points;

[0122] Finally, taking the minimum sum of the distances of the nearest distance points as the optimization objective, construct an optimization objective function, and perform iterative optimization through continuous assignment; until the sum of the distances of the nearest distance points is less than a preset distance threshold or the number of assignment iterations reaches a preset number threshold, take the external parameter matrix to be solved after the current assignment as the optimal external parameter calibration solution.

[0123] In the above two methods, an existing proximity search algorithm can be used to obtain the nearest distance points, such as the K-D tree (abbreviation of K-Dimensional Tree).

[0124] In order to further improve the data processing efficiency and accuracy, before synchronizing the original point cloud data and the original pose data in time to obtain the timestamp information after time synchronization, it may further include:

[0125] Perform filtering processing on the original point cloud data.

[0126] In this embodiment, voxel filtering and Gaussian statistical filtering can be adopted. Specifically, when performing filtering processing, voxel filtering processing can be performed first to downsample the point cloud data, then filter out the points with distance values greater than the preset distance threshold, and then use the Statistical Outlier Removal algorithm (that is, the statistical outlier removal algorithm) to remove some outlier points. For example, the distances of the 50 nearest adjacent points to each point can be examined. If the distance of a point exceeds one standard deviation of the average distance, then this point is marked as an outlier point and removed. Filtering the original point cloud data can greatly reduce the calculation amount of the subsequent nearest distance point pairs and improve the calculation efficiency.

[0127] The external parameter calibration device of the integrated navigation device and the lidar provided by the present invention will be described below. The external parameter calibration device of the integrated navigation device and the lidar described below can be mutually corresponding and referred to with the external parameter calibration method of the integrated navigation device and the lidar described above.

[0128] Figure 3 The external parameter calibration device of the integrated navigation device and the lidar provided by the embodiment of the present invention is shown. The device includes:

[0129] An acquisition module 310, configured to respectively acquire the original pose data acquired by the integrated navigation device and the original point cloud data acquired by the lidar;

[0130] A first processing module 320, configured to synchronize the original point cloud data and the original pose data in time to obtain the timestamp information after time synchronization;

[0131] The second processing module 330 is used to perform posture interpolation on the original posture data according to the time stamp information after time synchronization and the original point cloud data to obtain a posture interpolation result;

[0132] The third processing module 340 is used to calculate the initial value of the external parameter according to the original posture data, the original point cloud data and the posture interpolation result;

[0133] The fourth processing module 350 is used to solve and obtain the optimal extrinsic parameter calibration solution based on the initial value of the extrinsic parameter and the pre-constructed optimization objective function.

[0134] In an exemplary embodiment, the second processing module 330 is specifically configured to:

[0135] Respectively obtain the posture transformation matrix from the current moment to the initial moment and the corresponding timestamp information of each posture point in the original posture data, and obtain the virtual point corresponding to each radar point in the coordinate system of the integrated navigation device at the corresponding moment;

[0136] According to the time stamp information after time synchronization, adjacent pose points adjacent to the timestamp corresponding to each radar point in the original point cloud data are obtained from the original pose data;

[0137] Based on the timestamp information of adjacent pose points and the corresponding pose transformation matrix, the pose transformation matrix from the virtual point to the initial time point of the integrated navigation device is calculated as the pose interpolation result.

[0138] In an exemplary embodiment, the third processing module 340 is specifically configured to:

[0139] Select multiple pose points from the original pose data, and select a radar point corresponding to each pose point from the original point cloud data;

[0140] Substitute each pose point and the corresponding radar point into the pre-constructed transformation point coordinate expression to obtain multiple equations to be solved with the extrinsic parameter matrix as the unknown quantity. Combine the multiple equations to be solved and calculate the initial value of the extrinsic parameter.

[0141] It should be noted that the transformation point coordinate expression is used to describe that the transformation point coordinate is obtained by multiplying the pose transformation matrix from the virtual point to the initial time point of the integrated navigation device, the external parameter matrix to be determined, and the radar point coordinate.

[0142] It should be noted that the above-mentioned extrinsic parameter matrix to be determined may include rotation angles and translation amounts.

[0143] In an exemplary embodiment, the fourth processing module 350 is specifically used to construct an optimization objective function based on the initial value of the extrinsic parameter and take the sum of the distances of the closest points in the original point cloud data as the minimum as the optimization goal, and solve to obtain the optimal extrinsic parameter calibration solution.

[0144] Further, the above-mentioned fourth processing module 350 is specifically configured to:

[0145] Based on the initial value of the external parameters, assign values to the external parameter matrix to be solved;

[0146] According to the assigned external parameter matrix to be solved and the transformation point coordinate expression, obtain the transformation points corresponding to each radar point in the original point cloud data in the coordinate system at the initial moment of the integrated navigation device, and obtain a set of transformation points;

[0147] Respectively obtain at least two nearest neighboring transformation points corresponding to each transformation point in the set of transformation points, and calculate the distances between each transformation point and its corresponding at least two neighboring transformation points;

[0148] Sum up the distances between each transformation point and its corresponding at least two neighboring transformation points to obtain the sum of the distances of the nearest distance points;

[0149] Taking the minimum sum of the distances of the nearest distance points as the optimization objective, construct an optimization objective function, and perform iterative optimization through continuous value assignment;

[0150] Until the sum of the distances of the nearest distance points is less than the preset distance threshold or the number of value assignment iterations reaches the preset number threshold, take the currently assigned external parameter matrix to be solved as the optimal external parameter calibration solution.

[0151] Further, the above-mentioned fourth processing module 350 is specifically configured to:

[0152] Based on the initial value of the external parameters, assign values to the external parameter matrix to be solved;

[0153] According to the assigned external parameter matrix to be solved and the transformation point coordinate expression, obtain the transformation points corresponding to each radar point in the current scan frame of the original point cloud data in the coordinate system at the initial moment of the integrated navigation device, and use them as the first transformation points;

[0154] Determine the radar point with the shortest distance for each radar point in the current scan frame in the next scan frame, and obtain the transformation points corresponding to the radar points with the shortest distance in the coordinate system at the initial moment of the integrated navigation device, and use them as the second transformation points;

[0155] Take the first transformation point and the second transformation point as the nearest distance point pairs, and combine all the nearest distance point pairs in the original point cloud data into a set of nearest distance point pairs;

[0156] Calculate the sum of the distances of all the nearest distance point pairs in the set of nearest distance point pairs to obtain the sum of the distances of the nearest distance points;

[0157] Taking the minimum sum of the distances of the nearest distance points as the optimization objective, construct an optimization objective function, and perform iterative optimization through continuous value assignment;

[0158] When the sum of the distances to the closest points is less than a preset distance threshold or the assigned iteration count reaches a preset count threshold, the current externally calibrated matrix after assignment is used as the optimal externally calibrated solution.

[0159] In an exemplary embodiment, the externally calibrated device for the combined navigation device and the lidar may further include a filtering module, which is specifically configured to perform filtering processing on the original point cloud data.

[0160] It can be seen that the externally calibrated device for the combined navigation device and the lidar provided by the embodiment of the present invention calculates a relatively ideal initial externally calibrated value through the third processing module, which can effectively improve the calibration efficiency and calibration accuracy; at the same time, the device directly estimates the pose of the lidar by the interpolation method through the second processing module, improving the calibration efficiency and reducing the computational complexity; in addition, the filtering module performs filtering processing on the point cloud data collected by the lidar, effectively improving the calibration efficiency and reducing the calibration time cost.

[0161] In addition, the embodiment of the present invention further provides a working machine, which uses the externally calibrated method for the combined navigation device and the lidar in any of the above, and can realize the high-precision positioning and perception functions during the operation process.

[0162] It can be understood that the working machine mentioned in this embodiment may be an excavator, and of course, it may also be other working machines that need to install high-precision positioning and perception devices.

[0163] In addition, the embodiment of the present invention further provides an externally calibrated system for the combined navigation device and the lidar, and the system includes:

[0164] The lidar to be calibrated, which is used to collect the original point cloud data of the target object;

[0165] The combined navigation device to be calibrated, which is used to collect the original pose data of the target object;

[0166] A controller, which is used to respectively obtain the original pose data and the original point cloud data; synchronize the original point cloud data and the original pose data in time to obtain the timestamp information after time synchronization; perform pose interpolation on the original pose data according to the timestamp information after time synchronization and the original point cloud data to obtain a pose interpolation result; calculate an initial externally calibrated value according to the original pose data, the original point cloud data and the pose interpolation result; and solve to obtain the optimal externally calibrated solution based on the initial externally calibrated value and the pre-constructed optimization objective function.

[0167] Next, taking an excavator as an example, the implementation scheme of the externally calibrated system for the combined navigation device and the lidar will be described in detail.

[0168] The hardware structure framework of the calibrated system built in this embodiment is as Figure 4As shown in the figure, the hardware includes the excavator power supply, a controller (model can be Nuvo-7160GC), an RTK / IMU integrated navigation device (model can be CGI-610), a 3D lidar (model can be VLP-32C), a voltage regulator, a step-down transformer, a mouse and keyboard, a display screen, etc. The software part of this calibration system includes the operating systems of Ubuntu18.04 and ROS melodic, the underlying driver software, and the c++ programming language software, etc.

[0169] When installing the calibration system hardware, install the body of the RTK integrated navigation device CGI-610 and the controller Nuvo-7160GC at the rear position of the seat in the excavator cab. Install the RTK antenna of the integrated navigation device at the rear position of the upper body of the excavator. The distance between the two RTK antennas can be set as large as possible to obtain data with better signal strength. Install the lidar VLP-32C on the top of the cab or at a position above the top of the cab through a bracket. Install the mouse, keyboard, and display screen in front of the seat in the cab through mounting brackets.

[0170] As Figure 4 shown in the figure, the excavator power supply outputs 24V regulated voltage through a voltage regulator to supply power to the step-down transformer, the controller Nuvo-7160GC, as well as the mouse and keyboard. The step-down transformer outputs 12V voltage to supply power to the RTK integrated navigation device CGI-610, the lidar VLP-32C, and the display screen.

[0171] After the hardware installation is completed, start the excavator to move forward at a constant speed and turn on the above-built system. Operate the excavator to perform some non-planar motions to obtain attitude change data. Specifically, the excavator bucket can be controlled to support on the ground for pitching and rolling motions, so that there are large angular changes in the pitch angle, roll angle, and heading angle.

[0172] Then input the original pose data collected by the RTK integrated navigation device CGI-610 and the original point cloud data collected by the lidar VLP-32C into the controller Nuvo-7160GC, and use the calibration technology to realize the offline extrinsic parameter calibration of the RTK integrated navigation device CGI-610 and the lidar VLP-32C. The algorithm flow of the extrinsic parameter calibration process is as Figure 5 shown in the figure.

[0173] See Appendix Figure 5, First, read the raw data collected by the RTK integrated navigation device CGI-610, i.e., the CGI-610 data, and perform GPCHC data protocol parsing on it to obtain the position (i.e., WGS84 coordinates) and attitude data of the CGI-610, including the timestamp, longitude, latitude, and altitude (i.e., longitude, latitude, and height), and three attitude angles (i.e., pitch angle, roll angle, and heading angle). Then, perform coordinate transformation on the parsed WGS84 coordinates to obtain the pose data in the CGI-610 coordinate system at the initial moment t0.

[0174] Then, read the raw data collected by the lidar VLP-32C, i.e., the VLP-32C data, and perform data parsing to obtain the point cloud data in the pointcloud2 format. After that, perform filtering on the obtained point cloud data. When performing filtering, first perform voxel filtering to downsample the point cloud data; then filter out the points with a distance value greater than 50m; then use the Statistical Outlier Removal algorithm to remove some outlier points. In this embodiment, the distances of the 50 nearest adjacent points to each point are examined. If the distance of a point exceeds one standard deviation above the average distance, the point is marked as an outlier and removed. After filtering, the computational amount of subsequent nearest distance point pairs can be greatly reduced, improving the computational efficiency.

[0175] Next, first eliminate the delay time difference between the two sensors through time alignment, and then synchronize the time of the RTK integrated navigation device CGI-610 and the lidar VLP-32C. Since there is only one counter representing the TOH (Top Of Hour) time inside the lidar VLP-32C, the TOH time consists of two parts. One part is the number of minutes + seconds starting from the TOH time, and the other part is the number of microseconds. Moreover, the lidar cannot represent the time above the whole hour.

[0176] The NMEA GPGGA / GPRMC statements in the RTK integrated navigation device CGI-610 provide the minute part and second part of the UTC time. Therefore, the timestamp synchronization can be performed by reading this part of the information. For the lidar, use the RTK / IMU integrated navigation pps time alignment function to obtain accurate timestamps. At the hardware level, it is necessary to connect the pps signal of the RTK integrated navigation device CGI-610 to the lidar VLP-32C through the serial port, and consider the underlying driver of the lidar VLP-32C and the timestamp calculation mechanism of the lidar VLP-32C point cloud points to calculate the timestamp of a single lidar point.

[0177] After that, perform pose interpolation operations. Denote the coordinate system of the RTK integrated navigation device CGI-610 at the initial moment t0 as the world coordinate system, and calculate each pose point of the RTK integrated navigation device CGI-610 at the current moment tk Pose transformation matrix \(T_{t0,t}\) to the initial time \(t0\) k ;

[0178] Assume that each point \(p\) of the lidar i The timestamp relative to the initial time of the RTK integrated navigation device CGI-610 is \(t\) p (i.e., the timestamp after time synchronization);

[0179] In the RTK integrated navigation device CGI-610, the two timestamps closest to \(t\) p are \(t\) k 、\(t\) k+1 , and satisfy \(t\) k ≤ \(t\) p < \(t\) k+1 , then the transformation from time \(t\) k to the initial time \(t0\) in the RTK integrated navigation device CGI-610 is \(T_{t0,t}\) k , and the transformation from time \(t\) k+1 to the initial time \(t0\) is \(T_{t0,t}\) k+1 ;

[0180] Based on the above information, the pose transformation matrix \(T_{t0,t}\) from the virtual point to the point at the initial time of the integrated navigation device can be obtained p to achieve pose interpolation.

[0181] Based on \(t\) p , \(t\) k , \(t\) k+1 , \(T_{t0,t}\) k , \(T_{t0,t}\) k+1 and the pose interpolation result \(T_{t0,t}\) p , the pose point \(q\) of each lidar point \(p\) i in the coordinate system at the initial time of the RTK / IMU integrated navigation device can be estimated i . That is, for each lidar point in each scan frame, the corresponding more accurate position and attitude values are matched through the calculated timestamp.

[0182] Then, calculate the initial value of the external parameters. After completing time synchronization and pose interpolation, a relatively ideal initial value of the external parameters is calculated. This initial value of the external parameters can effectively improve the efficiency of subsequent external parameter optimization and obtain a more accurate external parameter solution. Combining the attached Figure 2 , in this embodiment, the five distances \(d1\), \(d2\), \(d3\), \(d4\), \(d5\) can be set to 5 meters, 10 meters, 15 meters, 20 meters, and 25 meters respectively. The calculation formula for the pose point \(q\) i is shown in the following formula:

[0183]

[0184] where \(i = 0, 1, \ldots, 5\), \(T_0\) is the initial value of the extrinsic parameters. represents the pose transformation matrix of the lidar point \(p\) i in the coordinate system of the integrated navigation device at the initial moment; \(T\cdot p\) i represents the lidar point \(p\) i converted to the virtual point coordinates in the coordinate system of the integrated navigation device at the current moment; \(T_{t_0t}\) p \(\cdot T\cdot p\) i represents the lidar point \(p\) i converted to the pose point coordinates in the coordinate system of the integrated navigation device at the initial moment.

[0185] Therefore, six unknowns are solved with six equations, and thus the initial value of the extrinsic parameters \(T_0(\alpha_0, \beta_0, \gamma_0, b\) 01 , b 02 , b 03 ) can be obtained.

[0186] Finally, with the minimum sum of the distances of the closest points as the optimization goal, an optimization objective function model is constructed, and the optimal extrinsic parameter calibration solution \(T\) is obtained through iterative optimization.

[0187] The optimal extrinsic parameter calibration solution \(T\) obtained in this embodiment can make the point cloud data of the lidar have the highest self - coincidence degree after being transformed to the coordinate system (i.e., the world coordinate system) of the RTK / IMU integrated navigation device at the initial moment \(t_0\).

[0188] Figure 6 An example of the physical structure diagram of an electronic device is shown in Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640. Among them, the processor 610, the communication interface 620, and the memory 630 complete communication with each other through the communication bus 640. The processor 610 can call the logical instructions in the memory 630 to execute the extrinsic parameter calibration method for the integrated navigation device and the lidar. The method includes: respectively obtaining the original pose data collected by the integrated navigation device and the original point cloud data collected by the lidar; synchronizing the time of the original point cloud data and the original pose data to obtain the timestamp information after time synchronization; performing pose interpolation on the original pose data according to the timestamp information after time synchronization and the original point cloud data to obtain the pose interpolation result; calculating the initial value of the extrinsic parameters according to the original pose data, the original point cloud data, and the pose interpolation result; and solving to obtain the optimal extrinsic parameter calibration solution based on the initial value of the extrinsic parameters and the pre - constructed optimization objective function.

[0189] In addition, when the logical instructions in the above-mentioned memory 630 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0190] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the method for calibrating the external parameters of the combined navigation device and the lidar provided by the above-mentioned various methods. The method includes: for the same target object, respectively obtaining the original pose data collected by the combined navigation device and the original point cloud data collected by the lidar; synchronizing the original point cloud data and the original pose data in time to obtain the timestamp information after time synchronization; according to the timestamp information after time synchronization and the original point cloud data, performing pose interpolation on the original pose data to obtain a pose interpolation result; calculating an initial value of the external parameters according to the original pose data, the original point cloud data, and the pose interpolation result; and solving to obtain an optimal solution for calibrating the external parameters based on the initial value of the external parameters and a pre-constructed optimization objective function.

[0191] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the method for calibrating the external parameters of the combined navigation device and the lidar provided by the above-mentioned various methods. The method includes: for the same target object, respectively obtaining the original pose data collected by the combined navigation device and the original point cloud data collected by the lidar; synchronizing the original point cloud data and the original pose data in time to obtain the timestamp information after time synchronization; according to the timestamp information after time synchronization and the original point cloud data, performing pose interpolation on the original pose data to obtain a pose interpolation result; calculating an initial value of the external parameters according to the original pose data, the original point cloud data, and the pose interpolation result; and solving to obtain an optimal solution for calibrating the external parameters based on the initial value of the external parameters and a pre-constructed optimization objective function.

[0192] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0193] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calibrating the external parameters of a combined navigation device and a lidar, characterized in that include: Respectively obtain the original pose data collected by the integrated navigation device and the original point cloud data collected by the laser radar; Time-synchronize the original point cloud data with the original pose data to obtain time-synchronized timestamp information; Performing posture interpolation on the original posture data according to the time stamp information after the time synchronization and the original point cloud data to obtain a posture interpolation result; Calculate the initial value of the external parameter according to the original posture data, the original point cloud data and the posture interpolation result; Based on the initial value of the external parameter and the pre-constructed optimization objective function, an optimal external parameter calibration solution is obtained; Wherein, the solution based on the initial value of the extrinsic parameter and the pre-constructed optimization objective function is to obtain the optimal extrinsic parameter calibration solution, including: assigning values to the extrinsic parameter matrix to be obtained based on the initial value of the extrinsic parameter; obtaining the transformation point corresponding to each radar point in the original point cloud data in the coordinate system of the combined navigation device at the initial moment according to the assigned extrinsic parameter matrix to be obtained and the transformation point coordinate expression, and obtaining a transformation point set; obtaining at least two nearest neighboring transformation points corresponding to each transformation point in the transformation point set, and calculating the distance between each transformation point and its at least two corresponding neighboring transformation points; summing the distances between each transformation point and its at least two corresponding neighboring transformation points to obtain the sum of the distances of the nearest distance points; taking the minimum sum of the distances of the nearest distance points as the optimization goal, constructing the optimization objective function, and searching for the best through continuous assignment iteration; until the sum of the distances of the nearest distance points is less than a preset distance threshold or the number of assignment iterations reaches a preset number threshold, the currently assigned extrinsic parameter matrix to be obtained is taken as the optimal extrinsic parameter calibration solution.

2. The external parameter calibration method of a combined navigation device and a lidar according to claim 1, wherein, According to the time stamp information after the time synchronization and the original point cloud data, the original pose data is subjected to pose interpolation to obtain a pose interpolation result, including: Respectively obtain the posture transformation matrix from the current moment to the initial moment and the corresponding timestamp information of each posture point in the original posture data, and obtain the virtual point corresponding to each radar point in the coordinate system of the integrated navigation device at the corresponding moment; Acquire, from the original pose data, adjacent pose points adjacent to the timestamp corresponding to each radar point in the original point cloud data according to the time stamp information after the time synchronization; Based on the timestamp information of the adjacent pose points and the corresponding pose transformation matrix, a pose transformation matrix from the virtual point to the initial time point of the integrated navigation device is calculated as a pose interpolation result.

3. A method for calibrating the external parameters of a combined navigation device and a lidar according to claim 1, characterized in that The initial value of the external parameter is calculated according to the original posture data, the original point cloud data and the posture interpolation result, including: Selecting a plurality of pose points from the original pose data, and selecting a radar point corresponding to each of the pose points from the original point cloud data; Substitute each pose point and the corresponding radar point into the pre-constructed transformation point coordinate expression to obtain multiple equations to be solved with the extrinsic parameter matrix as the unknown quantity, and combine the multiple equations to be solved to calculate the initial value of the extrinsic parameter.

4. A method for calibrating the external parameters of a combined navigation device and a lidar according to claim 3, characterized in that, The transformation point coordinate expression is used to describe that the transformation point coordinates are calculated through the pose transformation matrix from the virtual point to the initial moment point of the integrated navigation device, the pre-constructed external parameter matrix to be solved, and the radar point coordinates.

5. An external parameter calibration device for a combined navigation device and a lidar, characterized in that It includes: An acquisition module, configured to respectively acquire the original pose data acquired by the integrated navigation device and the original point cloud data acquired by the lidar; A first processing module, configured to synchronize the time of the original point cloud data and the original pose data to obtain the timestamp information after time synchronization; A second processing module, configured to perform pose interpolation on the original pose data according to the timestamp information after time synchronization and the original point cloud data to obtain a pose interpolation result; A third processing module, configured to calculate an initial value of the external parameter according to the original pose data, the original point cloud data, and the pose interpolation result; A fourth processing module, configured to solve the optimal external parameter calibration solution based on the initial value of the external parameter and the pre-constructed optimization objective function; The fourth processing module is further configured to assign a value to the external parameter matrix to be solved based on the initial value of the external parameter; according to the assigned external parameter matrix to be solved and the transformation point coordinate expression, obtain the transformation point corresponding to each radar point in the original point cloud data in the coordinate system of the initial moment of the integrated navigation device, and obtain a transformation point set; respectively obtain at least two nearest neighboring transformation points corresponding to each transformation point in the transformation point set, and calculate the distance between each transformation point and its corresponding at least two neighboring transformation points; sum the distances between each transformation point and its corresponding at least two neighboring transformation points to obtain the sum of the distances of the nearest distance points; construct an optimization objective function with the minimum sum of the distances of the nearest distance points as the optimization objective, and perform continuous assignment iteration optimization; until the sum of the distances of the nearest distance points is less than a preset distance threshold or the number of assignment iteration times reaches a preset number threshold, use the currently assigned external parameter matrix to be solved as the optimal external parameter calibration solution.

6. An operating machine, characterized in that, This construction machine uses an external parameter calibration method for an integrated navigation device and a lidar according to any one of claims 1 to 4.

7. An external parameter calibration system for a combined navigation device and a lidar, characterized in that It includes: A lidar to be calibrated, configured to acquire the original point cloud data of the target object; An integrated navigation device to be calibrated, configured to acquire the original pose data of the target object; A controller is configured to respectively obtain the original pose data and the original point cloud data; synchronize the time of the original point cloud data and the original pose data to obtain timestamp information after time synchronization; perform pose interpolation on the original pose data according to the timestamp information after time synchronization and the original point cloud data to obtain a pose interpolation result; calculate an initial value of the external parameter based on the original pose data, the original point cloud data, and the pose interpolation result; and assign a value to the external parameter matrix to be solved based on the initial value of the external parameter; obtain a transformed point corresponding to each radar point in the original point cloud data in the coordinate system at the initial moment of the integrated navigation device according to the transformed point coordinate expression after the value assignment to the external parameter matrix to be solved, so as to obtain a set of transformed points; respectively obtain at least two nearest neighboring transformed points corresponding to each transformed point in the set of transformed points, and calculate the distance between each transformed point and its corresponding at least two neighboring transformed points; sum up the distances between each transformed point and its corresponding at least two neighboring transformed points to obtain the sum of the distances of the nearest distance points; construct an optimization objective function with the minimum sum of the distances of the nearest distance points as the optimization objective, and perform iterative optimization by continuous value assignment; until the sum of the distances of the nearest distance points is less than a preset distance threshold or the number of value assignment iterations reaches a preset number threshold, take the external parameter matrix to be solved after the current value assignment as the optimal external parameter calibration solution.

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