A method and system for calibrating external parameters of multi-line laser radar on water

Abstract

The present invention relates to a method and system for calibrating external parameters of a multi-line laser radar on water, and belongs to the field of laser radar external parameter calibration. The method includes: obtaining the hull posture data and buoy point cloud data of an unmanned ship; converting the buoy point cloud data into buoy point cloud data in a map coordinate system based on the horizontal plane; determining the conversion relationship from the laser radar coordinate system to the map coordinate system based on the buoy point cloud data; determining the external parameters of the laser radar based on the conversion relationship from the laser radar coordinate system to the map coordinate system and the hull posture data; and completing the calibration of the laser radar based on the external parameters of the laser radar. The present invention uses a conical buoy to help increase the amount of point cloud data of the laser radar on water, and the conical buoy can provide more stable and reliable features for the point cloud data matching body obtained by the laser radar, improve the accuracy of the point cloud data matching, and thus help improve the accuracy of the laser radar external parameter calibration.

Description

Technical Field

[0001] The present invention relates to the field of laser radar external parameter calibration, and in particular to a method and system for calibrating external parameters of a multi-line laser radar on water. Background Art

[0002] When a LiDAR-based perception system is operating, it is necessary to calibrate the relative position relationship between the LiDAR and the unmanned vessel, namely the extrinsic parameters (Extreme parameters include rotation parameters and translation parameters; rotation parameters include roll, pitch, and yaw; translation parameters include x, y, and z). Calibration of the LiDAR extrinsic parameters of an unmanned vessel must be performed on the water surface. However, there is currently no LiDAR calibration method specifically for unmanned vessels in a water environment. The LiDAR extrinsic parameter calibration of an unmanned vessel is generally based on the calibration method used for ground unmanned vehicles.

[0003] Currently, there are many extrinsic parameter calibration methods for ground-based LiDARs. For example, the extrinsic parameter calibration method for multi-line LiDARs on unmanned vehicles, which relies on landmarks, requires placing specific calibration targets in the calibration environment. The three-dimensional coordinates of the calibration points in both the vehicle and radar coordinate systems are then calculated based on a preset conversion formula to obtain parameter calculation results. Based on these parameter calculation results, the extrinsic parameters of the multi-line LiDAR are determined. This method requires the vehicle to be calibrated in a fixed scene and requires the placement of more than 12 calibration targets, making the process cumbersome. Another method for extrinsic parameter calibration of multi-line LiDARs on unmanned vehicles without landmarks is essentially an optimization process, focusing on constructing the objective function to be optimized. This process involves obtaining the motion increment between two frames of LiDAR data in the vehicle coordinate system while the vehicle is in motion, and simultaneously obtaining a motion estimate in the LiDAR coordinate system by matching the LiDAR data. Using the LiDAR extrinsic parameters, the rotations of the LiDAR and vehicle coordinate systems can be converted to the same coordinate system. A homogeneous linear overdetermined system of equations is constructed and solved by the least-squares problem to obtain the extrinsic parameters.

[0004] Currently, calibrating the lidar extrinsic parameters of an unmanned vessel requires the vessel to move autonomously or remotely along a specific trajectory on the water surface. During this motion, point cloud data of the surrounding environment is collected, and the vessel's position data is recorded in real time. Once sufficient data is collected, it can be used to perform calculations and solve the problem to obtain the lidar extrinsic parameters. This calculation process is essentially an optimization process, focusing on constructing the objective function to be optimized. The motion increment between two frames of lidar data in the unmanned vessel coordinate system is obtained, and the motion estimate in the lidar coordinate system is obtained by matching the lidar data. Using the lidar extrinsic parameters, the rotation of the lidar coordinate system and the rotation of the unmanned vessel coordinate system are converted to the same coordinate system. A homogeneous linear overdetermined system of equations is constructed and solved by the least squares problem to obtain the extrinsic parameters.

[0005] By analyzing the above process, we can see that when optimizing the lidar's extrinsic parameters, the objective function has two constraints, one of which is motion estimation based on the principle of matching between two frames of point clouds. Therefore, the accuracy of point cloud matching will affect the calibration accuracy of the lidar's extrinsic parameters. When calibrating the lidar in an aquatic environment, due to the extremely high dielectric constant of water (83.83 at 10°C), the absorption rate of electromagnetic waves is high, and the echo intensity of the laser hitting the water surface is very low. Therefore, the lidar can only obtain a small number of point clouds on the open water surface. These point clouds do not produce sufficient features, making it impossible to effectively match the two frames of point clouds, resulting in reduced matching accuracy, which will seriously affect the accuracy of the lidar's extrinsic parameter calibration.

[0006] Another constraint is the incremental motion of the UAV's coordinate system between two frames of LiDAR data. Since the UAV moves in a planar motion on the water surface, the changes in its roll, pitch, and height (z) are relatively small, making them insufficiently stimulated and unable to capture sufficient valid data. This, in turn, affects the calibration accuracy of the LiDAR's extrinsic parameters for roll, pitch, and height (z). Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for calibrating the external parameters of a multi-line laser radar on water, so as to solve the problem of low calibration accuracy of the laser radar external parameter calibration method in the prior art.

[0008] To achieve the above object, the present invention provides the following solutions:

[0009] A method for calibrating external parameters of a multi-line laser radar on water, comprising:

[0010] Obtain the hull posture data of the unmanned ship and the buoy point cloud data;

[0011] Based on the horizontal plane, the buoy point cloud data is converted into buoy point cloud data in a map coordinate system;

[0012] Determining a conversion relationship from a lidar coordinate system to a map coordinate system based on the buoy point cloud data;

[0013] Determine the external parameters of the laser radar based on the conversion relationship between the laser radar coordinate system and the map coordinate system and the hull posture data; the external parameters of the laser radar include rotation parameters and translation parameters; the rotation parameters include roll angle, pitch angle and yaw angle; the translation parameters include translation parameters in the x-direction, translation parameters in the y-direction and translation parameters in the z-direction;

[0014] The laser radar is calibrated according to the external parameters of the laser radar.

[0015] Optionally, the step of obtaining the hull pose data of the unmanned vessel and the point cloud data of the laser radar further includes:

[0016] Arrange the lidar extrinsic calibration scene.

[0017] Optionally, the arrangement of the laser radar external parameter calibration scene specifically includes:

[0018] Three conical buoys are arranged within a preset range from the hull of the unmanned boat; the buoys have the same shape, weight, material and volume.

[0019] Optionally, the obtaining of the hull posture data of the unmanned vessel and the buoy point cloud data specifically includes:

[0020] Within a preset time, the unmanned boat is made to perform an 8-shaped motion around the buoy and the hull posture data of the unmanned boat and the point cloud data of the buoy are collected by the laser radar on the unmanned boat.

[0021] Optionally, after obtaining the hull pose data of the unmanned vessel and the buoy point cloud data, the following steps are also included:

[0022] The hull posture data of the unmanned ship and the buoy point cloud data are preprocessed to obtain preprocessed hull posture data and preprocessed buoy point cloud data.

[0023] Optionally, the preprocessing of the hull posture data of the unmanned ship and the buoy point cloud data specifically includes:

[0024] Performing time synchronization processing on the hull posture data of the unmanned vessel and the buoy point cloud data;

[0025] Performing filtering on the buoy point cloud data to obtain filtered buoy point cloud data;

[0026] The filtered buoy point cloud data and the hull posture data of the unmanned vessel are combined into a data queue.

[0027] Optionally, determining the conversion relationship from the lidar coordinate system to the map coordinate system based on the buoy point cloud data specifically includes:

[0028] According to the formula Determine the conversion relationship roll', pitch' and t from the lidar coordinate system to the map coordinate system z ; where x = [roll'pitch't z ] T , roll' is the conversion relationship of roll angle, pitch' is the conversion relationship of pitch angle, t z is the translation transformation relationship from the lidar coordinate system to the map coordinate system; is the height of the i-th point cloud data in the map coordinate system; L is the set of filtered buoy point cloud data, and i is the i-th point cloud data.

[0029] Optionally, determining the external parameters of the lidar according to the conversion relationship from the lidar coordinate system to the map coordinate system and the hull posture data specifically includes:

[0030] According to the conversion relationship between the laser radar coordinate system and the map coordinate system, roll', pitch' and t z And the pre-processed hull posture data, using the formula Determine the roll angle roll, pitch angle pitch and translation parameter z in the z direction; where, The conversion relationship from laser radar to map coordinate system; is the laser radar external parameter matrix; is the pre-processed hull posture data;

[0031] The roll angle roll, the pitch angle pitch, and the translation parameter z in the z direction are substituted into the lidar extrinsic parameter matrix, the three-dimensional extrinsic parameter calibration is converted into a two-dimensional plane calibration, and the yaw angle yaw, the translation parameter x in the x direction, and the translation parameter y in the y direction are determined.

[0032] An external parameter calibration system for a multi-line laser radar on water, comprising:

[0033] The data acquisition module is used to obtain the hull posture data of the unmanned ship and the buoy point cloud data;

[0034] A data conversion module, used to convert the buoy point cloud data into buoy point cloud data in a map coordinate system based on a horizontal plane;

[0035] A conversion relationship determination module, configured to determine a conversion relationship from a lidar coordinate system to a map coordinate system based on the buoy point cloud data;

[0036] an external parameter determination module, configured to determine the external parameters of the laser radar based on the conversion relationship between the laser radar coordinate system and the map coordinate system and the hull posture data; the external parameters of the laser radar include rotation parameters and translation parameters; the rotation parameters include roll angle, pitch angle and yaw angle; the translation parameters include translation parameters in the x-direction, translation parameters in the y-direction and translation parameters in the z-direction;

[0037] The calibration module is used to complete the calibration of the laser radar according to the external parameters of the laser radar.

[0038] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0039] This invention increases the number of point cloud features recognized by LiDAR by placing conical buoys on the water surface. Furthermore, during the computational solution, constraints based on the water surface environment are added to decouple the LiDAR extrinsic calibration process. Roll, pitch, and z are first calculated using water surface characteristics. Once the extrinsic parameters roll, pitch, and z are determined, the three-dimensional calibration is converted to a two-dimensional one. Yaw, x, and y are then calculated based on point cloud matching and ship motion constraints. This completes LiDAR calibration and improves its extrinsic calibration accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A flow chart of an external parameter calibration method for a multi-line laser radar on water provided by the present invention;

[0042] Figure 2 The layout diagram of the water surface lidar calibration scene provided by the present invention;

[0043] Figure 3 Flowchart for obtaining calibration data for a water surface lidar provided by the present invention;

[0044] Figure 4 The flowchart of buoy point cloud data filtering provided by the present invention;

[0045] Figure 5 This is a flow chart of an external parameter calibration method for a multi-line laser radar on water in a specific embodiment of the present invention;

[0046] Figure 6 This is a structural diagram of the external parameter calibration system for a multi-line underwater laser radar provided by the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] The purpose of the present invention is to provide a method and system for calibrating the external parameters of a multi-line laser radar on water, so as to solve the problem of low calibration accuracy of the laser radar external parameter calibration method in the prior art.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Figure 1 This is a flow chart of a method for calibrating external parameters of a multi-line laser radar on water provided by the present invention. Figure 5 FIG. 1 is a flow chart of an external parameter calibration method for a multi-line laser radar on water in a specific embodiment of the present invention. Figure 1 and Figure 5 As shown, a method for calibrating external parameters of a multi-line laser radar on water includes:

[0051] Step 101: Obtain the hull pose data of the unmanned vessel and the buoy point cloud data.

[0052] In a specific embodiment, the obtaining of the hull posture data of the unmanned ship and the point cloud data of the laser radar further includes:

[0053] Arrange the lidar extrinsic calibration scene.

[0054] In a specific embodiment, the arrangement of the laser radar extrinsic parameter calibration scene specifically includes:

[0055] Three conical buoys are arranged within a preset range from the hull of the unmanned boat; the buoys have the same shape, weight, material and volume.

[0056] In practical applications, the lidar external parameter calibration scene layout is first performed, such as Figure 2 As shown, three conical buoys are placed 50-80 meters around the ship (depending on the parameters of the LiDAR used). The conical buoys must be of the same shape, weight, material, and volume to ensure that their exposed portions are at the same height. Because water has a low reflectivity for laser light, while the buoys have a high reflectivity for laser light, the LiDAR can detect the point cloud of the exposed portion of the buoys.

[0057] In a specific embodiment, obtaining the hull posture data of the unmanned vessel and the buoy point cloud data specifically includes:

[0058] Within a preset time, the unmanned boat is made to perform an 8-shaped motion around the buoy and the hull posture data of the unmanned boat and the point cloud data of the buoy are collected by the laser radar on the unmanned boat.

[0059] In practical applications, after the calibration scene is arranged, data acquisition is performed, such as Figure 3The unmanned vessel was made to move in an ∞-shaped pattern at a low speed (less than 1.5 m / s) within the range of the three buoys for 2 minutes to obtain the vessel pose data and buoy point cloud data required for calibration.

[0060] In a specific embodiment, after obtaining the hull posture data of the unmanned vessel and the buoy point cloud data, the method further includes:

[0061] The hull posture data of the unmanned ship and the buoy point cloud data are preprocessed to obtain preprocessed hull posture data and preprocessed buoy point cloud data.

[0062] In a specific embodiment, the preprocessing of the hull posture data of the unmanned vessel and the buoy point cloud data specifically includes:

[0063] The hull posture data of the unmanned ship and the buoy point cloud data are time-synchronized.

[0064] The buoy point cloud data is filtered to obtain filtered buoy point cloud data.

[0065] The filtered buoy point cloud data and the hull posture data of the unmanned vessel are combined into a data queue.

[0066] In practical applications, data synchronization is first performed to synchronize the buoy point cloud data with the unmanned vessel hull posture data, and then the buoy point cloud data is filtered. The buoy point cloud data filtering flow chart is as follows: Figure 4 As shown, water has a very low reflectivity for lasers, but impurities in the water inevitably reflect the laser, generating some point cloud clutter. Waves and splashes generated by system movement can also reflect the laser, generating point clouds. These point cloud data have low laser reflection intensity, and this clutter can be filtered out by setting a threshold for the point cloud echo intensity. Furthermore, point clouds that are too close to the ship (any x, y, or z value is less than 10 meters) or too far away (any x, y, or z value is greater than 150 meters) are unnecessary and can be removed.

[0067] The filtered buoy point cloud data and the time-synchronized hull posture data are formed into a one-to-one corresponding data queue.

[0068] Step 102: Based on the horizontal plane, the buoy point cloud data is converted into buoy point cloud data in a map coordinate system.

[0069] Calculate roll, pitch, and z according to the constraints of the water surface environment.

[0070] The horizontal plane is used as the reference, that is, the map coordinate system. The point cloud is converted to the map coordinate system as follows:

[0071]

[0072] In the above formula, yaw', pitch', and roll' are the rotation transformation relationships from the lidar coordinate system to the map coordinate system, and t x , t y , t z is the translation transformation relationship from the lidar coordinate system to the map coordinate system, [X m Y m Z m ] T is the coordinate of the buoy point cloud data in the map coordinate system, [X l Y l Z l ] T is the coordinate of the buoy point cloud data in the laser radar coordinate system. The height of the buoy point cloud projected to the map coordinate system is:

[0073] Z m =-sin(pitch')*X l +cos(pitch')sin(roll')*Y l +cos(pitch')cos(roll')*Z l +t z

[0074] From the above formula, we can get that the height of the original point cloud projected to the map coordinates is only equal to pitch', roll', t z According to the characteristics of the water surface environment, the point clouds at the bottom of the three buoys are all above the water surface when converted to map coordinates, and the point clouds at the bottom of the three buoys are just on the horizontal plane, that is, the height Z m =0.

[0075] Step 103: Determine the conversion relationship from the lidar coordinate system to the map coordinate system based on the buoy point cloud data.

[0076] In a specific embodiment, determining the conversion relationship from the lidar coordinate system to the map coordinate system based on the buoy point cloud data specifically includes:

[0077] According to the formula Determine the conversion relationship roll', pitch' and t from the lidar coordinate system to the map coordinate system z ; where x = [roll'pitch't z ] T , roll' is the conversion relationship of roll angle, pitch' is the conversion relationship of pitch angle, t z is the translation transformation relationship from the lidar coordinate system to the map coordinate system; is the height of the i-th point cloud data in the map coordinate system; L is the set of filtered buoy point cloud data, and i is the i-th point cloud data.

[0078] In practical applications, we construct an optimization problem. For the point cloud of three buoys, we transform it into the map coordinate system and calculate the height Z of all points. m Sum Optimize x = [roll'pitch't z ] T ,make Minimum and arbitrary Z m ≥0:

[0079]

[0080] Solve the above optimization problem to get the roll', pitch', t of the laser radar to the map coordinate system z .

[0081] Step 104: Determine the external parameters of the laser radar based on the conversion relationship from the laser radar coordinate system to the map coordinate system and the hull posture data; the external parameters of the laser radar include rotation parameters and translation parameters; the rotation parameters include roll angle, pitch angle and yaw angle; the translation parameters include translation parameters in the x direction, translation parameters in the y direction and translation parameters in the z direction.

[0082] In a specific embodiment, determining the external parameters of the laser radar based on the conversion relationship from the laser radar coordinate system to the map coordinate system and the hull posture data specifically includes:

[0083] According to the conversion relationship between the laser radar coordinate system and the map coordinate system, roll', pitch' and t z And the pre-processed hull posture data, using the formula Determine the roll angle roll, pitch angle pitch and translation parameter z in the z direction; where, The conversion relationship from laser radar to map coordinate system; is the laser radar external parameter matrix; is the preprocessed hull pose data.

[0084] The roll angle roll, the pitch angle pitch, and the translation parameter z in the z direction are substituted into the lidar extrinsic parameter matrix, the three-dimensional extrinsic parameter calibration is converted into a two-dimensional plane calibration, and the yaw angle yaw, the translation parameter x in the x direction, and the translation parameter y in the y direction are determined.

[0085] In practical applications, solving the above optimization problem yields roll', pitch', t zAfter that, the rotation and translation matrix from the hull coordinates to the map coordinates is obtained, that is, the hull pose data It has been obtained through the above data collection steps. Therefore, according to the formula, The external parameter matrix of the laser radar relative to the hull can be calculated The roll, pitch, and z in . The calculation process is as follows:

[0086] The conversion relationship from laser radar to map coordinate system.

[0087]

[0088] is the lidar extrinsic parameter matrix, and roll, pitch, yaw, x, y, and z are the extrinsic parameters to be determined.

[0089]

[0090] It is the hull-to-map pose data, obtained through the data acquisition step, and all parameters are known.

[0091]

[0092] According to the formula have:

[0093]

[0094] According to the above formula, roll, pitch, and z can be solved.

[0095] Since the lidar extrinsic parameters roll, pitch, and z have already been calculated, the remaining extrinsic parameters to be calculated are the ship's translation parameters x and y on the XY plane, and the rotation angle yaw around the Z axis. By substituting the roll, pitch, and z parameters calculated above into the lidar extrinsic parameter matrix, the three-dimensional extrinsic parameter calibration can be converted to a two-dimensional plane calibration to calculate the yaw, x, and y parameters. The process for calculating the two-dimensional plane parameters yaw, x, and y is as follows.

[0096] First, the point clouds of the three buoys are projected onto the horizontal plane, that is, the height Z of all buoy point clouds is m =0.

[0097] On a two-dimensional plane, the unmanned ship has the following equation when it is at any two positions b1 and b2: in is the coordinate of point p under the laser radar when the unmanned ship is at position b1, is the coordinate of point p under the laser radar when the unmanned ship is at position b2, is the laser radar external parameter matrix [yaw, x, y], is the hull posture data when the hull is at position b1, is the hull posture data when the hull is at position b2.

[0098] Transform the above formula:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] Can be simplified to in is the laser radar external parameter matrix [yaw, x, y] to be determined, is the relative pose of the lidar between two different positions [yaw l 、x l 、y l ], which can be obtained through the lidar two-frame point cloud matching algorithm. For the hull pose transformation at two different positions [yaw b 、x b 、y b ], obtained by the data acquisition step; The position transformation relationship between the laser radar and point p when the unmanned ship is at position b1; is the position conversion relationship between the laser radar and the map when the unmanned ship is at position b1; for In the collected calibration data, select more than four data to obtain more than three and To form an overdetermined system of equations, solve it using the least squares method and get [yaw, x, y].

[0105] Step 105: Complete the calibration of the laser radar based on the external parameters of the laser radar. Through step 104, the six external parameters of the laser radar [yaw, roll, pitch, x, y, z] have all been calculated and the calibration is completed.

[0106] The present invention establishes an underwater calibration environment for the external parameters of the LiDAR by arranging buoys. During the data acquisition process, the ship is made to move in a figure-eight pattern within the range of the buoys, and the time is limited to 2 minutes. A decoupled method is used to calculate the external parameter data of the LiDAR, converting the three-dimensional calibration into a two-dimensional calibration. The roll, pitch, and z parameters of the LiDAR are calculated by using the constraints of the water surface environment. The use of conical buoys overcomes the problem of low reflectivity of circular buoys to the LiDAR. The use of three buoys reduces the number of buoys, facilitates deployment and recovery, and improves calibration efficiency.

[0107] Figure 6 This is a structural diagram of an external parameter calibration system for a multi-line laser radar on water provided by the present invention, such as Figure 6 As shown, the system includes:

[0108] The data acquisition module 601 is used to acquire the hull posture data of the unmanned vessel and the buoy point cloud data.

[0109] The data conversion module 602 is used to convert the buoy point cloud data into buoy point cloud data in a map coordinate system based on the horizontal plane.

[0110] The conversion relationship determination module 603 is used to determine the conversion relationship from the lidar coordinate system to the map coordinate system based on the buoy point cloud data.

[0111] The extrinsic parameter determination module 604 is configured to determine the extrinsic parameters of the lidar based on the conversion relationship between the lidar coordinate system and the map coordinate system and the vessel pose data. The extrinsic parameters of the lidar include rotation parameters and translation parameters. The rotation parameters include roll angle, pitch angle, and yaw angle. The translation parameters include x-, y-, and z-direction translation parameters.

[0112] The calibration module 605 is used to complete the calibration of the laser radar according to the external parameters of the laser radar.

[0113] This invention establishes an underwater calibration environment for unmanned vessels by deploying buoys. The use of conical buoys helps increase the number of LiDAR point clouds captured above water. Furthermore, the conical buoys provide more stable and reliable features for the LiDAR point cloud matching volume, improving the accuracy of point cloud matching. This also enhances the accuracy of motion estimation during point cloud matching, thus helping to improve the accuracy of LiDAR extrinsic calibration.

[0114] This invention utilizes a decoupled data calculation method that exploits the characteristics of the water surface environment. First, based on the fact that the water surface does not reflect laser light, the point cloud data for the conical buoy is calculated to be above the water surface, and the point cloud at the bottom of the conical buoy is located just above the horizontal plane, with height z being 0. This environmental characteristic is used to decouple the three parameters: roll, pitch, and z. An optimization objective function is then constructed and solved for these three parameters. This solves the problem of low calibration accuracy due to the inability to fully excite the roll, pitch, and z parameters due to the planar motion of the unmanned vessel.

[0115] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0116] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for calibrating external parameters of a multi-line laser radar on water, characterized in that: include: Obtain the hull posture data of the unmanned ship and the buoy point cloud data; Based on the horizontal plane, the buoy point cloud data is converted into buoy point cloud data in a map coordinate system; Determining a conversion relationship from a laser radar coordinate system to a map coordinate system based on the buoy point cloud data; the conversion relationship from the laser radar coordinate system to the map coordinate system includes a conversion relationship of a roll angle, a conversion relationship of a pitch angle, and a translation conversion relationship; According to the formula Determine the conversion relationship roll', pitch' and t from the lidar coordinate system to the map coordinate system z ; where x = [roll'pitch't z ] T , roll' is the conversion relationship of roll angle, pitch' is the conversion relationship of pitch angle, t z is the translation transformation relationship from the lidar coordinate system to the map coordinate system; is the height of the i-th point cloud data in the map coordinate system; L is the set of filtered buoy point cloud data, and i is the i-th point cloud data; The point cloud is converted to the map coordinate system as follows: Among them, yaw', pitch', and roll' are the rotation transformation relationships from the lidar coordinate system to the map coordinate system, and t x , t y , t z is the translation transformation relationship from the lidar coordinate system to the map coordinate system, [X m Y m Z m ] T is the coordinate of the buoy point cloud data in the map coordinate system, [X l Y l Z l ] T is the coordinate of the buoy point cloud data in the lidar coordinate system; Determine the external parameters of the laser radar based on the conversion relationship between the laser radar coordinate system and the map coordinate system and the hull posture data; the external parameters of the laser radar include rotation parameters and translation parameters; the rotation parameters include roll angle, pitch angle and yaw angle; the translation parameters include translation parameters in the x-direction, translation parameters in the y-direction and translation parameters in the z-direction; Determine the external parameters of the laser radar based on the conversion relationship from the laser radar coordinate system to the map coordinate system and the hull posture data, specifically including: According to the conversion relationship between the laser radar coordinate system and the map coordinate system, roll', pitch' and t z And the preprocessed hull posture data, using the formula Determine the roll angle roll, pitch angle pitch and translation parameter z in the z direction; where, The conversion relationship from laser radar to map coordinate system; is the laser radar external parameter matrix; is the pre-processed hull posture data; Substituting the roll angle roll, the pitch angle pitch, and the translation parameter z in the z direction into the lidar extrinsic parameter matrix, converting the three-dimensional extrinsic parameter calibration into a two-dimensional plane calibration, and determining the yaw angle yaw, the translation parameter x in the x direction, and the translation parameter y in the y direction; The laser radar is calibrated according to the external parameters of the laser radar.

2. The method for calibrating external parameters of a multi-line laser radar on water according to claim 1, characterized in that: The acquisition of the hull pose data of the unmanned ship and the point cloud data of the laser radar also includes: Arrange the lidar extrinsic calibration scene.

3. The method for calibrating external parameters of a multi-line laser radar on water according to claim 2, characterized in that: The arrangement of the laser radar external parameter calibration scene specifically includes: Three conical buoys are arranged within a preset range from the hull of the unmanned boat; the buoys have the same shape, weight, material and volume.

4. The method for calibrating external parameters of a multi-line laser radar on water according to claim 3, characterized in that: The obtaining of the hull posture data of the unmanned vessel and the buoy point cloud data specifically includes: Within a preset time, the unmanned boat is made to perform an 8-shaped motion around the buoy and the hull posture data of the unmanned boat and the point cloud data of the buoy are collected by the laser radar on the unmanned boat.

5. The method for calibrating external parameters of a multi-line laser radar on water according to claim 1, characterized in that: After obtaining the unmanned vessel's hull pose data and buoy point cloud data, the following steps are also included: The hull posture data of the unmanned ship and the buoy point cloud data are preprocessed to obtain preprocessed hull posture data and preprocessed buoy point cloud data.

6. The method for calibrating external parameters of a multi-line laser radar on water according to claim 5, characterized in that: The preprocessing of the hull posture data of the unmanned ship and the buoy point cloud data specifically includes: Performing time synchronization processing on the hull posture data of the unmanned vessel and the buoy point cloud data; Performing filtering on the buoy point cloud data to obtain filtered buoy point cloud data; The filtered buoy point cloud data and the hull posture data of the unmanned vessel are combined into a data queue.

7. An external parameter calibration system for a multi-line laser radar on water, characterized in that: include: The data acquisition module is used to obtain the hull posture data of the unmanned ship and the buoy point cloud data; A data conversion module, used to convert the buoy point cloud data into buoy point cloud data in a map coordinate system based on a horizontal plane; a conversion relationship determination module, configured to determine a conversion relationship from a lidar coordinate system to a map coordinate system based on the buoy point cloud data; the conversion relationship from the lidar coordinate system to the map coordinate system includes a conversion relationship of a roll angle, a conversion relationship of a pitch angle, and a translation conversion relationship; According to the formula Determine the conversion relationship roll', pitch' and t from the lidar coordinate system to the map coordinate system z ; where x = [roll'pitch't z ] T , roll' is the conversion relationship of roll angle, pitch' is the conversion relationship of pitch angle, t z is the translation transformation relationship from the lidar coordinate system to the map coordinate system; is the height of the i-th point cloud data in the map coordinate system; L is the set of filtered buoy point cloud data, and i is the i-th point cloud data; The point cloud is converted to the map coordinate system as follows: Among them, yaw', pitch', and roll' are the rotation transformation relationships from the lidar coordinate system to the map coordinate system, and t x , t y , t z is the translation transformation relationship from the lidar coordinate system to the map coordinate system, [X m Y m Z m ] T is the coordinate of the buoy point cloud data in the map coordinate system, [X l Y l Z l ] T is the coordinate of the buoy point cloud data in the lidar coordinate system; an external parameter determination module, configured to determine the external parameters of the laser radar based on the conversion relationship between the laser radar coordinate system and the map coordinate system and the hull posture data; the external parameters of the laser radar include rotation parameters and translation parameters; the rotation parameters include roll angle, pitch angle and yaw angle; the translation parameters include translation parameters in the x-direction, translation parameters in the y-direction and translation parameters in the z-direction; Determine the external parameters of the laser radar based on the conversion relationship from the laser radar coordinate system to the map coordinate system and the hull posture data, specifically including: According to the conversion relationship between the laser radar coordinate system and the map coordinate system, roll', pitch' and t z And the preprocessed hull posture data, using the formula Determine the roll angle roll, pitch angle pitch and translation parameter z in the z direction; where, The conversion relationship from laser radar to map coordinate system; is the laser radar external parameter matrix; is the pre-processed hull posture data; Substituting the roll angle roll, the pitch angle pitch, and the translation parameter z in the z direction into the lidar extrinsic parameter matrix, converting the three-dimensional extrinsic parameter calibration into a two-dimensional plane calibration, and determining the yaw angle yaw, the translation parameter x in the x direction, and the translation parameter y in the y direction; The calibration module is used to complete the calibration of the laser radar according to the external parameters of the laser radar.

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