LiDAR external parameter calibration method, system, computer equipment and readable storage medium
阅读说明：本技术 LiDAR外部参数标定方法、系统、计算机设备及可读存储介质 (LiDAR external parameter calibration method, system, computer equipment and readable storage medium ) 是由 刘前飞 刘康 张三林 王振男 张志德 于 2020-08-06 设计创作，主要内容包括：本发明公开了一种基于ROS2的LiDAR外部参数标定方法,包括：获取LiDAR原始点云数据；对LiDAR原始点云数据进行过滤处理,以提取目标点云数据；在ROS2中构建虚拟模型；根据LiDAR外部参数、目标点云数据及虚拟模型在ROS2中构建图形用户界面；在构建图形用户界面中实时调整LiDAR外部参数,以获取外部标定参数。本发明还公开了一种基于ROS2的LiDAR外部参数标定系统、计算机设备及计算机可读存储介质。本发明通过直接调节LiDAR外部参数来确定理想的外部标定参数,避免了在车辆坐标系空间或者图像空间的数据测量带来的额外参数误差,大大缩短了标定的流程和时间,缩减了硬件成本。(The invention discloses a LiDAR external parameter calibration method based on ROS2, which comprises the following steps: acquiring LiDAR original point cloud data; filtering LiDAR original point cloud data to extract target point cloud data; building a virtual model in the ROS 2; constructing a graphical user interface in the ROS2 according to the LiDAR external parameters, the target point cloud data and the virtual model; LiDAR external parameters are adjusted in real-time in building a graphical user interface to obtain external calibration parameters. The invention also discloses a ROS 2-based LiDAR external parameter calibration system, computer equipment and a computer-readable storage medium. The invention determines ideal external calibration parameters by directly adjusting LiDAR external parameters, avoids extra parameter errors caused by data measurement in a vehicle coordinate system space or an image space, greatly shortens the calibration process and time, and reduces the hardware cost.)
1. A LiDAR external parameter calibration method based on ROS2 is characterized by comprising the following steps:
acquiring LiDAR original point cloud data;
filtering the LiDAR raw point cloud data to extract target point cloud data;
building a virtual model in the ROS 2;
constructing a graphical user interface in the ROS2 according to the LiDAR external parameters, the target point cloud data and the virtual model;
adjusting the LiDAR external parameters in real-time in the build graphical user interface to obtain external calibration parameters; wherein the step of adjusting LiDAR external parameters in real-time in a graphical user interface to obtain external calibration parameters includes: adjusting a Roll angle parameter, a Pitch angle parameter and a Z parameter in the LiDAR external parameters in real time in the graphical user interface, and when a ground point cloud plane formed by target point cloud data is superposed with a virtual plane, taking the current Roll angle parameter as a Roll angle calibration parameter, taking the current Pitch angle parameter as a Pitch angle calibration parameter and taking the current Z parameter as a Z calibration parameter; and adjusting the Yaw angle parameter in the LiDAR external parameters in real time in the graphical user interface, and taking the current Yawangle parameter as a Yaw angle calibration parameter when a road formed by the target point cloud data is parallel to the side direction of the virtual vehicle body along the point cloud direction.
2. The method of ROS 2-based LiDAR extrinsic parameter calibration of claim 1, wherein the step of acquiring LiDAR raw point cloud data comprises:
selecting a horizontal road without obstacle interference;
keeping the body of the target vehicle parallel to the road edge;
LiDAR raw point cloud data based on ROS2 is collected by LiDAR located on a target vehicle.
3. The method of ROS 2-based LiDAR extrinsic parameter calibration of claim 1, wherein the step of filtering LiDAR raw point cloud data to extract target point cloud data comprises:
respectively acquiring the height value of each original point cloud data;
respectively judging whether the height values are higher than a preset height threshold value,
if so, deleting the original point cloud data corresponding to the height value,
and if not, extracting the original point cloud data corresponding to the height value, and taking the original point cloud data corresponding to the height value as target point cloud data.
4. The method of ROS 2-based LiDAR extrinsic parameter calibration according to claim 1, wherein the step of building a virtual model in ROS2 comprises:
constructing a virtual plane in a grid diagram of the ROS2, wherein the virtual plane is used for representing a road plane;
constructing a virtual vehicle in a grid map of the ROS2, the virtual vehicle representing a target vehicle;
and constructing a virtual coordinate system in a grid map of the ROS2 according to the virtual plane and the virtual vehicle.
5. The method of ROS 2-based LiDAR extrinsic parameter calibration according to claim 1, wherein the step of constructing a graphical user interface in the ROS2 from the LiDAR extrinsic parameters, the target point cloud data, and the virtual model comprises:
invoking the QT library in ROS2 to build a graphical user interface;
establishing an incidence relation among LiDAR external parameters, the target point cloud data and the virtual model;
and displaying the association relation on the graphical user interface.
6. A LiDAR outside parameter calibration system based on ROS2, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring LiDAR (light detection and ranging) original point cloud data;
the filtering module is used for filtering the LiDAR original point cloud data to extract target point cloud data;
a model building module for building a virtual model in ROS 2;
the interface construction module is used for constructing a graphical user interface in the ROS2 according to the LiDAR external parameters, the target point cloud data and the virtual model;
an adjustment module to adjust the LiDAR external parameters in real-time in the build graphical user interface to obtain external calibration parameters; specifically, the adjusting module includes: the ground adjusting unit is used for adjusting the Roll angle parameter, the Pitch angle parameter and the Z parameter in the LiDAR external parameters in real time in the graphical user interface, when a ground point cloud plane formed by target point cloud data is superposed with a virtual plane, the current Roll angle parameter is used as a Roll angle calibration parameter, the current Pitch angle parameter is used as a Pitch angle calibration parameter, and the current Z parameter is used as a Z calibration parameter; and the road edge adjusting unit is used for adjusting the Yaw angle parameters in the LiDAR external parameters in real time in the graphical user interface, and when the road edge point cloud direction formed by the target point cloud data is parallel to the side direction of the virtual vehicle body, the current Yaw angle parameters are used as the Yaw angle calibration parameters.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 5.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
Technical Field
The invention relates to the technical field of automatic driving, in particular to a LiDAR external parameter calibration method based on ROS2, a LiDAR external parameter calibration system based on ROS2, computer equipment and a computer-readable storage medium.
Background
In recent years, research on the automatic driving technique has become more and more active. Especially, the environmental sensing technology based on LiDAR (light detection And Ranging), which is laser detection And measurement, i.e. LiDAR, has also gained wide attention in both academic And industrial fields.
The LiDAR external parameter calibration mainly has the function of converting a target information coordinate system detected by a LiDAR sensor into a unified vehicle coordinate system; wherein the LiDAR extrinsic parameters include coordinate system spatial rotation parameters (Yaw angle, Pitchangle, Roll angle) and coordinate system spatial translation matrix parameters (X, Y, Z); all sensor parameter calibrations, including LiDAR, have been the basis for the functional implementation in the automotive field, and their importance is self-evident. Currently, there are two main methods commonly used for LiDAR external parameter calibration:
(1) a combined calibration method based on LiDAR and a camera. The calibration method comprises the steps of firstly obtaining internal parameters of a camera through calibrating the camera, then matching the point cloud position of the LiDAR with target pixel points in the image space of the camera to obtain rotation and translation matrix parameters of the LiDAR relative to the camera space, and finally completing LiDAR external parameter calibration. However, the calibration method has a complex flow, an additional camera sensor is required, and the error of LiDAR external parameter calibration is strongly related to the error of camera parameter calibration; under the condition that the camera does not exist on the vehicle or the parameter calibration of the camera has errors, the LiDAR calibration parameter errors are larger, and the LiDAR performance for the perception of the environment around the intelligent driving is limited.
(2) A method for solving a calibration matrix for external calibration tool measurements directly by LiDAR. The method directly carries out position coordinates under a LiDAR coordinate system space and a vehicle coordinate system space on a plurality of orderly-arranged external calibration tools (such as a cone, a calibration rod and the like) through LiDAR, and then directly solves the rotation and translation matrix parameters of the LiDAR relative to the vehicle coordinate system to finish LiDAR external parameter calibration. The method does not need to rely on other sensors such as a camera and the like, but needs additional calibration tool assistance, and the final calibration parameter error is increased due to the measurement error on the target position vehicle data in the vehicle coordinate system.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a LiDAR external parameter calibration method, system, computer equipment and computer-readable storage medium based on ROS2, which can directly adjust LiDAR external parameters to determine ideal external calibration parameters and reduce errors.
In order to solve the technical problem, the invention provides a LiDAR external parameter calibration method based on ROS2, which comprises the following steps: acquiring LiDAR original point cloud data; filtering the LiDAR raw point cloud data to extract target point cloud data; building a virtual model in the
As an improvement of the above solution, the step of acquiring LiDAR raw point cloud data includes: selecting a horizontal road without obstacle interference; keeping the body of the target vehicle parallel to the road edge; LiDAR raw point cloud data based on ROS2 is collected by LiDAR located on a target vehicle.
As an improvement of the above scheme, the step of filtering the LiDAR raw point cloud data to extract target point cloud data includes: respectively acquiring the height value of each original point cloud data; and respectively judging whether the height value is higher than a preset height threshold value, if so, deleting the original point cloud data corresponding to the height value, if not, extracting the original point cloud data corresponding to the height value, and taking the original point cloud data corresponding to the height value as target point cloud data.
As an improvement to the above solution, the step of constructing the virtual model in the ROS2 includes: constructing a virtual plane in a grid diagram of the ROS2, wherein the virtual plane is used for representing a road plane; constructing a virtual vehicle in a grid map of the ROS2, the virtual vehicle representing a target vehicle; and constructing a virtual coordinate system in a grid map of the ROS2 according to the virtual plane and the virtual vehicle.
As an improvement of the above solution, the step of constructing a graphical user interface in the ROS2 according to the LiDAR external parameters, the target point cloud data, and the virtual model includes: invoking the QT library in ROS2 to build a graphical user interface; establishing an incidence relation among LiDAR external parameters, the target point cloud data and the virtual model; and displaying the association relation on the graphical user interface.
As an improvement of the above solution, the step of adjusting LiDAR external parameters in real time in a graphical user interface to obtain external calibration parameters includes: adjusting a Rollangle parameter, a Pitch angle parameter and a Z parameter in the LiDAR external parameters in real time in the graphical user interface, and when a ground point cloud plane formed by target point cloud data is superposed with a virtual plane, taking the current Rollangle parameter as a Rollangle calibration parameter, taking the current Pitch angle parameter as a Pitch angle calibration parameter and taking the current Z parameter as a Z calibration parameter; and adjusting the Yaw angle parameters in the LiDAR external parameters in real time in the graphical user interface, and when the road direction formed by the target point cloud data is parallel to the lateral direction of the virtual vehicle body, taking the current Yaw angle parameters as Yaw angle calibration parameters.
Correspondingly, the invention also provides a LiDAR external parameter calibration system based on the ROS2, which comprises: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring LiDAR (light detection and ranging) original point cloud data; the filtering module is used for filtering the LiDAR original point cloud data to extract target point cloud data; a model building module for building a virtual model in
As an improvement of the above solution, the adjusting module includes: the ground adjusting unit is used for adjusting the Roll angle parameter, the Pitch angle parameter and the Z parameter in the LiDAR external parameters in real time in the graphical user interface, when a ground point cloud plane formed by target point cloud data is superposed with a virtual plane, the current Roll angle parameter is used as a Rollangle calibration parameter, the current Pitch angle parameter is used as a Pitch angle calibration parameter, and the current Z parameter is used as a Z calibration parameter; and the road edge adjusting unit is used for adjusting the Yawagle parameters in the LiDAR external parameters in real time in the graphical user interface, and when the road edge point cloud direction formed by the target point cloud data is parallel to the side direction of the virtual vehicle body, the current Yaw angle parameters are used as the Yaw angle calibration parameters.
Correspondingly, the invention also provides computer equipment which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the LiDAR external parameter calibration method when executing the computer program.
Accordingly, the present invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the LiDAR extrinsic parameter calibration method described above.
The implementation of the invention has the following beneficial effects:
according to the method, the ROS2 and the QT are used for constructing the graphical user interface for debugging the LiDAR external parameters, so that the position of target point cloud data subjected to coordinate conversion can be displayed in real time, the process is simpler, and the calibration process and time are greatly shortened;
meanwhile, the ideal external calibration parameters are determined by directly adjusting the LiDAR external parameters, and the image space or the vehicle coordinate system space target does not need to be directly measured, so that extra parameter errors caused by data measurement in the vehicle coordinate system space or the image space are avoided, and the errors are smaller;
in addition, the invention does not need to rely on other additional sensors (such as a camera) or other auxiliary calibration tools (such as a cone), the parameter calibration method is simpler and quicker, and the hardware cost is reduced.
Drawings
FIG. 1 is a flowchart of an embodiment of a ROS 2-based LiDAR outside parameter calibration method of the present invention;
FIG. 2 is a schematic diagram of a virtual model in the present invention;
FIG. 3 is a location diagram of target point cloud data prior to LiDAR extrinsic parameter adjustment in accordance with the present invention;
FIG. 4 is a location chart of target point cloud data after adjustment of LiDAR external parameters in the present invention;
FIG. 5 is a schematic diagram of the structure of a ROS 2-based LiDAR outside parameter calibration system of the present invention;
FIG. 6 is a schematic diagram of the configuration of an adjustment module in the ROS 2-based LiDAR outside parameter calibration system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Referring to FIG. 1, FIG. 1 shows an embodiment of a ROS 2-based LiDAR outside parameter calibration method of the present invention, which includes:
s101, acquiring LiDAR original point cloud data;
specifically, the step of acquiring LiDAR raw point cloud data includes:
(1) selecting a horizontal road without obstacle interference;
(2) keeping the body of the target vehicle parallel to the road edge;
(3) LiDAR raw point cloud data based on ROS2 is collected by LiDAR located on a target vehicle. Wherein, the ROS2 refers to ROS (robot Operating System) version 2.0.
For example, a horizontal road with a length of 30 meters and a width of more than 10 meters can be selected, the body of the target vehicle is kept parallel to the road edge or the lane line, and no other obstacles interfere on the field; a piece of Rosbag data of ROS2, which is LiDAR raw point cloud data, is then recorded with LiDAR that has a fixed location installed on the target vehicle.
S102, filtering LiDAR original point cloud data to extract target point cloud data;
specifically, the step of filtering the LiDAR raw point cloud data to extract target point cloud data includes:
(1) respectively acquiring the height value of each original point cloud data;
(2) and respectively judging whether the height value is higher than a preset height threshold value, if so, deleting the original point cloud data corresponding to the height value, if not, extracting the original point cloud data corresponding to the height value, and taking the original point cloud data corresponding to the height value as target point cloud data.
Therefore, the LiDAR raw point cloud data higher than the preset height threshold is filtered by limiting the height value of the LiDAR raw point cloud data to obtain target point cloud data. The target point cloud data comprises road edge point cloud data and ground point cloud data.
S103, constructing a virtual model in the
specifically, the step of constructing the virtual model in the ROS2 includes:
(1) constructing a virtual plane in a grid diagram of the ROS2, wherein the virtual plane is used for representing a road plane;
as shown in fig. 2, a horizontal virtual plane is constructed in the grid diagram in the ROS2, and the thickness of the virtual plane does not exceed 2 cm.
(2) Constructing a virtual vehicle in a grid map of the ROS2, the virtual vehicle representing a target vehicle;
as shown in fig. 2, on the basis of the construction of the virtual plane, a virtual vehicle represented by a rectangular parallelepiped is further constructed in the grid diagram of the
(3) And constructing a virtual coordinate system in a grid map of the ROS2 according to the virtual plane and the virtual vehicle.
As shown in fig. 2, the virtual coordinate system includes an X axis, a Y axis and a Z axis, and the virtual plane center height is located at a position of the virtual coordinate system where the value of the Y axis is 0.
S104, constructing a graphical user interface in the ROS2 according to the LiDAR external parameters, the target point cloud data and the virtual model;
specifically, the step of constructing a graphical user interface in the ROS2 according to the LiDAR external parameters, the target point cloud data and the virtual model includes:
(1) invoking the QT library in ROS2 to build a graphical user interface;
invoking the QT library in the ROS2 may establish a graphical user interface (i.e., GUI interface) for adjusting LiDAR external parameters.
(2) Establishing an incidence relation among LiDAR external parameters, the target point cloud data and the virtual model;
the LiDAR external parameters include a Roll angle parameter, a Pitch angle parameter, a Yaw angle parameter, an X parameter, a Y parameter, and a Z parameter. Wherein the Roll angle parameter is Roll angle rotating around the X-axis, the Pitch angle parameter is Pitch angle rotating around the Y-axis, and the Yaw angle parameter is Yaw angle rotating around the Z-axis.
(3) And displaying the association relation on the graphical user interface.
According to the method, the graphical user interface is associated with the display program of the target point cloud data in the ROS2, so that the association relation among the LiDAR external parameters, the target point cloud data and the virtual model is established, the LiDAR external parameters are adjusted in the graphical user interface, and the position relation of the target point cloud data relative to the virtual vehicle is updated in real time.
S105, adjusting LiDAR external parameters in real time in the construction of the graphical user interface to obtain external calibration parameters.
It should be noted that the LiDAR extrinsic parameters and the virtual model are constrained with respect to each other, and specifically, the LiDAR extrinsic parameters are positive and negative according to the rule of the right-hand coordinate system:
assuming that the Roll angle parameter is θ (i.e., the Roll angle is θ), the three-dimensional coordinate matrix of the target point cloud data around the X axis has the following formula:
assuming that the Pitch angle parameter is α (assuming that the Pitch angle is α), the three-dimensional matrix conversion formula of the target point cloud data around the Y-axis coordinate is as follows:
assuming that the Yaw angle parameter is β (assuming that the Yaw angle is β), the three-dimensional coordinate transformation formula of the target point cloud data around the Z-axis is as follows:
specifically, the step of adjusting LiDAR external parameters in real-time in a graphical user interface to obtain external calibration parameters includes:
(1) adjusting a Roll angle parameter, a Pitch angle parameter and a Z parameter in real time in the graphical user interface, when a ground point cloud plane formed by target point cloud data is superposed with a virtual plane, taking the current Roll angle parameter as a Roll calibration parameter, taking the current Pitch angle parameter as a Pitch angle calibration parameter, and taking the current Z parameter as a Z calibration parameter;
(2) and adjusting the Yaw angle parameter in real time in the graphical user interface, and when a road formed by the target point cloud data is parallel to the lateral direction of the virtual vehicle body along the point cloud direction, taking the current Yaw angle parameter as a Yaw angle calibration parameter.
The target point cloud data comprises road edge point cloud data and ground point cloud data, wherein the road edge point cloud data can form road edge point cloud, and the direction of a line formed by the road edge point cloud is the direction of the road edge point cloud; the ground point cloud data can form a ground point cloud, and a plane formed by the ground point cloud is a ground point cloud plane.
Therefore, by continuously adjusting the Roll angle parameter, the Pitch angle parameter and the Z parameter, the ground point cloud plane can be superposed with the established virtual plane as much as possible, so that the proper Roll angle calibration parameter, Pitch angle calibration parameter and Z calibration parameter can be obtained. In addition, the point cloud direction of the road edge can be ensured to be parallel to the side direction of the constructed virtual vehicle body by continuously adjusting the Yaw angle parameters, so that proper Yaw angle calibration parameters are obtained.
In addition, the coordinate translation matrix parameters (such as X parameters and Y parameters) of the target point cloud data can be directly obtained through external measurement, and additional calibration is not needed.
As shown in FIG. 3, before LiDAR extrinsic parameter adjustment, the ground point cloud plane and the virtual plane have a certain angle, which proves that the values of the Pitch angle parameter and the Roll angle parameter are not 0.
As shown in FIG. 4, after the LiDAR extrinsic parameters are adjusted, the ground point cloud plane is substantially coincident with the virtual plane, and the curb point cloud orientation is substantially parallel to the virtual vehicle body lateral direction.
In conclusion, the graphical user interface for LiDAR external parameter debugging is constructed through the ROS2 and the QT, the position of target point cloud data after coordinate conversion can be displayed in real time, the process is simpler, and the calibration process and time are greatly shortened; meanwhile, the ideal external calibration parameters are determined by directly adjusting the LiDAR external parameters, and the image space or the vehicle coordinate system space target does not need to be directly measured, so that extra parameter errors caused by data measurement in the vehicle coordinate system space or the image space are avoided, and the errors are smaller; in addition, the invention does not need to rely on other additional sensors (such as a camera) or other auxiliary calibration tools (such as a cone), the parameter calibration method is simpler and quicker, and the hardware cost is reduced.
Referring to FIG. 5, FIG. 5 shows a specific structure of the ROS 2-based LiDAR outside
the system comprises an acquisition module 1 for acquiring LiDAR raw point cloud data. The acquisition module 1 may acquire a segment of ROS 2-based Rosbag data recorded by a LiDAR, which is LiDAR raw point cloud data.
And the
And the
And the
And the adjusting
As shown in fig. 6, specifically, the adjusting
a ground adjusting unit 51, configured to adjust a rollingle parameter, a Pitch angle parameter, and a Z parameter in the LiDAR external parameters in real time in the gui, and when a ground point cloud plane formed by target point cloud data coincides with a virtual plane, take the current Roll angle parameter as a Roll angle calibration parameter, take the current Pitch angle parameter as a Pitch angle calibration parameter, and take the current Z parameter as a Z calibration parameter;
and the road edge adjusting unit 52 is used for adjusting the Yawagle parameters in the LiDAR external parameters in real time in the graphical user interface, and when the road edge point cloud direction formed by the target point cloud data is parallel to the side direction of the virtual vehicle body, taking the current Yaw angle parameters as the Yaw angle calibration parameters.
The target point cloud data comprises road edge point cloud data and ground point cloud data, wherein the road edge point cloud data can form road edge point cloud, and the direction of a line formed by the road edge point cloud is the direction of the road edge point cloud; the ground point cloud data can form a ground point cloud, and a plane formed by the ground point cloud is a ground point cloud plane.
Therefore, by continuously adjusting the Roll angle parameter, the Pitch angle parameter and the Z parameter, the ground point cloud plane can be superposed with the established virtual plane as much as possible, so that the proper Roll angle calibration parameter, Pitch angle calibration parameter and Z calibration parameter can be obtained. In addition, the point cloud direction of the road edge can be ensured to be parallel to the side direction of the constructed virtual vehicle body by continuously adjusting the Yaw angle parameters, so that proper Yaw angle calibration parameters are obtained. In addition, the coordinate translation matrix parameters (such as X parameters and Y parameters) of the target point cloud data can be directly obtained through external measurement, and additional calibration is not needed.
Correspondingly, the invention also provides computer equipment which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the LiDAR external parameter calibration method when executing the computer program. Also, the present invention provides a computer readable storage medium having stored thereon a computer program that, when executed by a processor, performs the steps of the LiDAR outside parameter calibration method described above.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.