Laser radar dynamic calibration method and device based on high-precision positioning

Abstract

The application discloses a kind of based on high-precision positioning laser radar dynamic calibration method and its device, the method includes: obtaining the original measurement data of laser radar, and screening with preset condition;Based on the type of high-precision positioning lane line and line point, the lane line curve equation is fitted;The point-line correlation is carried out between the screened laser radar measurement data and the lane line curve equation, to associate the measurement value of laser radar with the lane line under high-precision map;Based on the above association, the azimuth and pitch angle in the original measurement data are corrected based on the azimuth and pitch angle calculated by the lane line curve equation;According to the installation parameters of the laser radar and the corrected azimuth and pitch angle, the misalignment angle and misalignment displacement of the laser radar are calculated.The application calibrates laser radar using high-precision positioning, uses the lane line of structured road as the true value of laser radar, and can effectively improve the calibration precision of laser radar.

Description

Technical Field

[0001] This invention relates to the field of lidar calibration technology, and in particular to a lidar dynamic calibration method and apparatus based on high-precision positioning. Background Technology

[0002] Current autonomous driving perception systems typically rely on the coupling of multiple sensors, such as millimeter-wave radar, cameras, and LiDAR, to obtain data from various sensors. This improves the accuracy of target localization and recognition, thereby enhancing the safety redundancy and stability of the autonomous driving system. Therefore, accurate calibration of LiDAR is fundamental for autonomous vehicles to accurately perceive the external environment; the higher the calibration accuracy of the LiDAR, the more accurately the point cloud data reflects the environment.

[0003] However, existing dynamic calibration and misalignment detection methods for lidar are based on estimating the installation angle deviation using the results of current angle measurements. Due to the lack of true values, the calibration accuracy of lidar is relatively low. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a dynamic calibration method and device for lidar based on high-precision positioning. The method uses high-precision positioning to calibrate lidar and takes the lane lines of structured roads as the true value of lidar, which can effectively improve the calibration accuracy of lidar.

[0005] To achieve the above objectives, embodiments of the present invention provide a dynamic calibration method for lidar based on high-precision positioning, comprising:

[0006] Acquire the raw measurement data from the lidar and filter it according to preset conditions;

[0007] Based on the type and location of lane lines under high-precision positioning, the equation of the lane line curve is obtained by fitting.

[0008] The filtered lidar measurement data is correlated with the lane line curve equation to link the lidar measurement values ​​with the lane lines under the high-precision map.

[0009] Based on the above correlation, the azimuth and pitch angles calculated using the lane line curve equation are used to correct the azimuth and pitch angles in the original measurement data.

[0010] Based on the installation parameters of the lidar and the corrected azimuth and elevation angles, the misalignment angle and misalignment displacement of the lidar are calculated.

[0011] As an improvement to the above solution, before acquiring the raw measurement data from the lidar, the following steps are also included:

[0012] The lidar is synchronized with the high-precision positioning module in time.

[0013] As an improvement to the above solution, before acquiring the raw measurement data from the lidar, the following steps are also included:

[0014] The vehicle speed and body attitude parameters are acquired, and it is determined whether the preset dynamic calibration conditions are met based on the vehicle speed and body attitude parameters; wherein, the body attitude parameters include acceleration, steering wheel angle, yaw rate and pitch rate.

[0015] As an improvement to the above solution, the preset condition is:

[0016] The original measurement data is static and the confidence level is greater than a preset threshold.

[0017] As an improvement to the above scheme, the process of fitting the lane line curve equation based on the lane line type and crossing point under high-precision positioning specifically includes:

[0018] High-precision positioning results are obtained in real time through a high-precision positioning module;

[0019] The high-precision positioning result is matched with a high-precision map to convert the real-world position into a position relative to the high-precision map.

[0020] The type and location of the lane lines matched based on the current location are obtained from the high-precision map broadcast. The lane line curve equation is obtained by fitting the curve with the vehicle center as the origin.

[0021] As an improvement to the above solution, the step of calculating the misalignment angle and misalignment displacement of the lidar based on the installation parameters of the lidar and the corrected azimuth and elevation angles specifically includes:

[0022] Based on the installation location parameters of the lidar, the corrected azimuth angle, and the corrected elevation angle, a system of equations is established:

[0023]

[0024]

[0025] Among them, Range n θ represents the distance in the original measurement data. n This represents the azimuth angle in the original measurement data; θ represents the pitch angle in the original measurement data; θ0 represents the azimuth angle at the time of installation. ψ0 represents the pitch angle during installation; θ represents the roll angle during installation. Δ Indicates the azimuth angle to be calibrated; Indicates the pitch angle to be calibrated; ψ Δ Indicates the roll angle to be calibrated; (xΔ y Δ , z Δ ) represents the positional deviation to be calibrated; θ n ′ Indicates the corrected azimuth angle; Indicates the corrected pitch angle;

[0026] The misalignment angle of the lidar is obtained by solving the system of equations using an estimation method. and misalignment displacement (x) Δ y Δ , z Δ ).

[0027] As an improvement to the above solution, the method further includes:

[0028] The vehicle attitude angle at time m is calculated as follows:

[0029]

[0030]

[0031]

[0032] Among them, YawAngle m PitchAngle m RollAngle m Let be the vehicle attitude angles at time m, yawRate, pitchRate, and rollRate be the yaw rate, pitch rate, and roll rate, respectively, and Δt be the update period for the vehicle attitude angles.

[0033] According to the formula:

[0034]

[0035]

[0036]

[0037] The misalignment angle of the lidar is filtered.

[0038] in, These are the filtered angles, and N is the number of accumulated frames. YawAngle is the misalignment angle calculated for the i-th frame. i PitchAngle i RollAngle i Let be the vehicle attitude angles at time i.

[0039] As an improvement to the above solution, the method further includes:

[0040] Calculate t i Time and t j The difference in vehicle attitude angle at each moment is:

[0041]

[0042]

[0043]

[0044] Among them, ΔYawAngle i-j For t i Time and t j The difference in yaw angle at any given moment, ΔPitchAngle i-j For t i Time and t j The difference in pitch angle at any given moment, ΔRollAngle i-j For t i Time and t j The difference in roll angle at any given moment, YawRate, PitchRate, and RollRate are the yaw rate, pitch rate, and roll rate, respectively;

[0045] A filtering algorithm is used to filter the misalignment angle of the lidar.

[0046] This invention also provides a dynamic calibration device for a lidar based on high-precision positioning, including a lidar and a data processing device, wherein the lidar and the data processing device are connected by communication.

[0047] The lidar sends its raw measurement data to the data processing device;

[0048] The data processing device is equipped with a calibration algorithm module, which performs the following processing during operation:

[0049] The original measurement data is filtered according to preset conditions; based on the type and points of the lane lines under high-precision positioning, the lane line curve equation is obtained by fitting.

[0050] The filtered lidar measurement data is correlated with the lane line curve equation to link the lidar measurement values ​​with the lane lines under the high-precision map.

[0051] Based on the above correlation, the azimuth and pitch angles calculated using the lane line curve equation are used to correct the azimuth and pitch angles in the original measurement data.

[0052] Based on the installation parameters of the lidar and the corrected azimuth and elevation angles, the misalignment angle and misalignment displacement of the lidar are calculated.

[0053] As an improvement to the above solution, the data processing device is also equipped with a high-precision positioning module and a high-precision map. The high-precision positioning module is used to acquire high-precision positioning results in real time.

[0054] As an improvement to the above scheme, the data processing device acts as a time synchronization master, sending a synchronization message, and the lidar acts as a time synchronization slave, responding to the synchronization message to synchronize the time axis of the lidar with the time of the data processing device.

[0055] This invention also provides a dynamic calibration device for lidar based on high-precision positioning, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the dynamic calibration method for lidar based on high-precision positioning described above.

[0056] Compared to existing technologies, the beneficial effects of the dynamic calibration method and apparatus for lidar based on high-precision positioning provided in this invention are as follows: The original measurement data of the lidar is acquired and filtered according to preset conditions; based on the type and points of the lane lines under high-precision positioning, a lane line curve equation is fitted; the filtered lidar measurement data is correlated with the lane line curve equation to associate the lidar measurement values ​​with the lane lines under the high-precision map; based on the above correlation, the azimuth and elevation angles in the original measurement data are corrected using the azimuth and elevation angles calculated from the lane line curve equation; and the misalignment angle and misalignment displacement of the lidar are calculated according to the installation parameters of the lidar and the corrected azimuth and elevation angles. This invention, by fitting the lane line curve equation to the type and points of the lane lines under high-precision positioning, and using the lane lines of the structured road as the true value for lidar calibration, can effectively improve the calibration accuracy of the lidar. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating a preferred embodiment of a dynamic calibration method for lidar based on high-precision positioning provided by the present invention.

[0058] Figure 2 This is a schematic diagram of a preferred embodiment of a dynamic calibration device for lidar based on high-precision positioning provided by the present invention;

[0059] Figure 3This is a schematic diagram of another preferred embodiment of a dynamic calibration device for lidar based on high-precision positioning provided by the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Please see Figure 1 , Figure 1 This is a flowchart illustrating a preferred embodiment of a dynamic calibration method for lidar based on high-precision positioning provided by the present invention. The dynamic calibration method for lidar based on high-precision positioning includes:

[0062] S1, acquire the raw measurement data of the lidar and filter it according to preset conditions;

[0063] S2, based on the type and points of the lane line under high-precision positioning, the equation of the lane line curve is obtained by fitting.

[0064] S3, perform point-to-line correlation between the filtered lidar measurement data and the lane line curve equation to associate the lidar measurement values ​​with the lane lines under the high-precision map;

[0065] S4. Based on the above correlation, the azimuth and pitch angles in the original measurement data are corrected using the azimuth and pitch angles calculated from the lane line curve equation.

[0066] S5. Based on the installation parameters of the lidar and the corrected azimuth and elevation angles, the misalignment angle and misalignment displacement of the lidar are calculated.

[0067] Specifically, this embodiment acquires the raw measurement data of the lidar, which includes at least distance, azimuth, and elevation angles. Measurement data meeting preset conditions is then selected from the raw data. Based on the type of lane lines and the points of travel under high-precision positioning, the lane line curve is fitted to obtain the lane line curve equation. Then, the selected lidar measurement data is correlated with the lane line curve equation to link the lidar measurements with the lane lines on the high-precision map. Based on this correlation, the azimuth angle calculated using the lane line curve equation is used to correct the azimuth angle in the raw measurement data, and the elevation angle calculated using the lane line curve equation is used to correct the elevation angle in the raw measurement data. Finally, based on the lidar's installation parameters and the corrected azimuth and elevation angles, the lidar's misalignment angle and misalignment displacement are calculated.

[0068] It should be noted that since the raw measurement data of the lidar can be incorporated into the domain controller for processing, and the domain controller has a high-precision positioning module and a high-precision map, the embodiment of the present invention obtains the lane line curve equation by fitting the type of lane line and the line point under high-precision positioning, and uses the lane line of the structured road as the true value of the lidar to calibrate the lidar, which can effectively improve the calibration accuracy of the lidar.

[0069] In another preferred embodiment, before acquiring the raw measurement data from the lidar in step S1, the method further includes:

[0070] The lidar is synchronized with the high-precision positioning module in time.

[0071] Specifically, in this embodiment, before acquiring the raw measurement data from the LiDAR, the high-precision positioning module in the domain controller is synchronized with the LiDAR in time. The autonomous driving domain controller, acting as the time synchronization master, sends a synchronization message, and the LiDAR, acting as the slave, responds to the synchronization request and aligns its own timeline with the domain controller. After the high-precision positioning module and the LiDAR are synchronized, the LiDAR adds a synchronized timestamp to the acquired raw measurement data, including distance, azimuth, and pitch angles, and transmits it to the domain controller. It should be noted that some LiDARs do not have pitch angle measurement capabilities and therefore do not output pitch angle data.

[0072] This embodiment uses Ethernet-based gPTP time synchronization. When the connection is CAN, AutoSarCAN-based time synchronization is used.

[0073] In another preferred embodiment, before acquiring the raw measurement data from the lidar in step S1, the method further includes:

[0074] The vehicle speed and body attitude parameters are acquired, and it is determined whether the preset dynamic calibration conditions are met based on the vehicle speed and body attitude parameters; wherein, the body attitude parameters include acceleration, steering wheel angle, yaw rate and pitch rate.

[0075] Specifically, before acquiring the raw measurement data from the lidar, this embodiment also acquires the vehicle speed and body attitude parameters, and determines whether the preset dynamic calibration conditions are met based on the vehicle speed and body attitude parameters. These dynamic calibration conditions can be set according to actual conditions. For example, the vehicle speed can be set to operate within a certain speed range, such as (10, 150) kph; the yaw rate can be less than a certain value, such as 60° / s; the pitch rate can be less than a certain value, such as 10° / s; the absolute value of acceleration can be less than a certain value, such as 2 m / s²; and the absolute value of steering wheel angle can be less than a certain value, such as 90°.

[0076] As a preferred embodiment, the preset conditions are:

[0077] The original measurement data is static and the confidence level is greater than a preset threshold.

[0078] Specifically, in this embodiment, the preset conditions are: the original measurement data is static, and the confidence level is greater than a preset threshold. After acquiring the original measurement data from the lidar, this embodiment filters out static points with high confidence (or high reflectivity) based on the attributes of the lidar point cloud.

[0079] In yet another preferred embodiment, step S2, which involves fitting the lane line curve equation based on the lane line type and crossing point under high-precision positioning, specifically includes:

[0080] S201, high-precision positioning results are obtained in real time through the high-precision positioning module;

[0081] S202, Match the high-precision positioning result with the high-precision map to convert the real-world position into a position relative to the high-precision map;

[0082] S203, obtain the type and location of the lane lines based on the current location matching broadcast by the high-precision map, and fit the lane line curve equation with the vehicle center as the origin.

[0083] Specifically, this embodiment uses a high-precision positioning module to acquire in real time satellite positioning signals from GNSS, RTK service signals, vehicle speed signals, visually perceived lane line and ground marking signals, triaxial acceleration and triaxial angular rate signals provided by a high-precision IMU, and road elements provided by an HD map, to obtain a fused centimeter-level high-precision positioning result. The high-precision positioning result is matched with a high-precision map using a built-in deflection plugin to convert the real-world position into a position relative to the high-precision map. The type and position points of the lane lines matched based on the current positioning, broadcast from the high-precision map, are obtained. Using the vehicle center as the origin, a cubic curve equation is fitted based on the lane line type and position points to obtain the lane line curve equation:

[0084] y = C0 + C1x + C2x 2 +C3x 3

[0085] Where x represents the longitudinal distance in the vehicle's rectangular coordinate system, y represents the lateral distance in the vehicle's rectangular coordinate system, and C0, C1, C2, and C3 are all constants.

[0086] By transforming the raw measurement data from the lidar and the lane curve equation into the same vehicle coordinate system, we obtain:

[0087] y = C0 + C1(x - x0) + C2(x - x0)2 +C3(x-x0) 3 +y0

[0088] Where x0 represents the longitudinal deviation between the two lane lines, and y0 represents the lateral deviation between the two lane lines. The original measurements (θ) in the polar coordinate system are then... n Range n The original LiDAR measurement data is transformed into the same coordinate system as the lane curve equation, and a preset threshold is used to associate the filtered raw LiDAR measurement data with the lane curve equation, retaining the measurement data that can be associated with the curve equation. The size of the threshold is usually positively correlated with the accuracy of the original measurement data. For points that have already been associated, due to the more accurate radar distance measurement, the distance measurement value (Range) of the original measurement data is set. n Substituting the lane curve equation, the corrected azimuth angle θ′ is calculated. n and pitch angle

[0089] In another preferred embodiment, step S5, based on the installation parameters of the lidar and the corrected azimuth and elevation angles, calculates the misalignment angle and misalignment displacement of the lidar, specifically including:

[0090] S501, establish a set of equations based on the installation position parameters of the lidar, the corrected azimuth angle, and the corrected elevation angle:

[0091]

[0092] Among them, Range n θ represents the distance in the original measurement data. n This represents the azimuth angle in the original measurement data; θ represents the pitch angle in the original measurement data; θ0 represents the azimuth angle at the time of installation. ψ0 represents the pitch angle during installation; θ represents the roll angle during installation. Δ Indicates the azimuth angle to be calibrated; Indicates the pitch angle to be calibrated; ψ Δ Indicates the roll angle to be calibrated; (x Δ y Δ , z Δ ) represents the positional deviation to be calibrated; θ′ n Indicates the corrected azimuth angle; Indicates the corrected pitch angle;

[0093] S502, Solve the equations using an estimation method to obtain the misalignment angle of the lidar. and misalignment displacement (x) Δ y Δ , z Δ).

[0094] Specifically, in this embodiment, based on the installation position parameters of the lidar and the corrected azimuth angle θ′... n and the corrected pitch angle Establish a system of equations:

[0095]

[0096] Among them, Range n θ represents the distance in the original measurement data. n This represents the azimuth angle in the original measurement data; θ represents the pitch angle in the original measurement data; θ0 represents the azimuth angle at the time of installation. ψ0 represents the pitch angle during installation; θ represents the roll angle during installation. Δ Indicates the azimuth angle to be calibrated; Indicates the pitch angle to be calibrated; ψ Δ Indicates the roll angle to be calibrated; (x Δ y Δ , z Δ ) represents the positional deviation to be calibrated; θ′ n Indicates the corrected azimuth angle; Indicates the corrected pitch angle;

[0097] The above equations are solved using estimation methods, such as the least squares method, to obtain the misalignment angle of the lidar. and misalignment displacement (x) Δ y Δ , z Δ ).

[0098] It should be noted that the result obtained here is... It incorporates the deflection angle caused by the non-linear motion of the vehicle body itself.

[0099] In yet another preferred embodiment, the method further includes:

[0100] The vehicle attitude angle at time m is calculated as follows:

[0101]

[0102]

[0103]

[0104] Among them, YawAngle m PitchAngle m RollAngle mLet be the vehicle attitude angles at time m, yawRate, pitchRate, and rollRate be the yaw rate, pitch rate, and roll rate, respectively, and Δt be the update period for the vehicle attitude angles.

[0105] According to the formula:

[0106]

[0107]

[0108]

[0109] The misalignment angle of the lidar is filtered.

[0110] in, These are the filtered angles, and N is the number of accumulated frames. YawAngle is the misalignment angle calculated for the i-th frame. i PitchAngle i RollAngle i Let be the vehicle attitude angles at time i.

[0111] Specifically, in this embodiment, the vehicle attitude angle is calculated by integrating the angular rate output by the IMU. Zero-point correction of the vehicle's angular rate monitors the steering wheel angle to within a certain value, and the offset is obtained by averaging the angular rates. This embodiment assumes the angular rate is unbiased.

[0112] The vehicle attitude angle at time m (referring to the m-th cycle of the angular velocity change from non-zero) is calculated as follows:

[0113]

[0114]

[0115]

[0116] Among them, YawAngle m PitchAngle m RollAngle m Let be the vehicle attitude angles at time m, where yawRate, pitchRate, and rollRate are the yaw rate, pitch rate, and roll rate, respectively, and Δt is the update period for the vehicle attitude angles.

[0117] Since the calculation frequency of the lidar misalignment angle is less than the attitude change frequency, for the i-th calculated value, YawAngle can be taken as the nearest value. i PitchAngle iand RollAngle i According to the formula:

[0118]

[0119]

[0120]

[0121] Perform multi-frame smoothing filtering.

[0122] in, These are the filtered angles, and N is the number of accumulated frames. YawAngle is the misalignment angle calculated for the i-th frame. i PitchAngle i RokkAngle i Let be the vehicle attitude angles at time i.

[0123] It should be noted that N can be determined by the required accuracy or time. A smaller N can be used for sudden misalignment detection, while a larger N can be used for dynamic calibration. Multi-frame smoothing filtering can also be accomplished by Kalman filtering or other filtering methods.

[0124] In yet another preferred embodiment, the method further includes:

[0125] Calculate t i Time and t j The difference in vehicle attitude angle at each moment is:

[0126]

[0127]

[0128]

[0129] Among them, ΔYawAngle i-j For t i Time and t j The difference in yaw angle at any given moment, ΔPitchAngle i-j For t i Time and t j The difference in pitch angle at any given moment, ΔRollAngle i-j For t i Time and t j The difference in roll angle at any given moment, YawRate, PitchRate, and RollRate are the yaw rate, pitch rate, and roll rate, respectively;

[0130] A filtering algorithm is used to filter the misalignment angle of the lidar.

[0131] Specifically, in this embodiment, compensating for the vehicle body attitude angle can also be achieved using state transition, i.e., calculating t i Time and t j The differences in vehicle attitude angles at each moment, ΔYawAngle, ΔPitchAngle, and ΔRollAngle:

[0132]

[0133]

[0134]

[0135] This facilitates the use of Kalman filtering or other filtering algorithms for multi-frame filtering.

[0136] Accordingly, the present invention also provides a dynamic calibration device for lidar based on high-precision positioning, which can realize all the processes of the dynamic calibration method for lidar based on high-precision positioning in the above embodiments.

[0137] Please see Figure 2 , Figure 2 This is a schematic diagram of a preferred embodiment of a dynamic calibration device for a lidar based on high-precision positioning provided by the present invention. The dynamic calibration device for a lidar based on high-precision positioning includes a lidar and a data processing device, and the lidar and the data processing device are communicatively connected.

[0138] The lidar sends its raw measurement data to the data processing device;

[0139] The data processing device is equipped with a calibration algorithm module, which performs the following processing during operation:

[0140] The original measurement data is filtered according to preset conditions; based on the type and points of the lane lines under high-precision positioning, the lane line curve equation is obtained by fitting.

[0141] The filtered lidar measurement data is correlated with the lane line curve equation to link the lidar measurement values ​​with the lane lines under the high-precision map.

[0142] Based on the above correlation, the azimuth and pitch angles calculated using the lane line curve equation are used to correct the azimuth and pitch angles in the original measurement data.

[0143] Based on the installation parameters of the lidar and the corrected azimuth and elevation angles, the misalignment angle and misalignment displacement of the lidar are calculated.

[0144] Preferably, the data processing device is further provided with a high-precision positioning module and a high-precision map, wherein the high-precision positioning module is used to acquire high-precision positioning results in real time.

[0145] Preferably, the data processing device acts as a time synchronization master, sending a synchronization message, and the lidar acts as a time synchronization slave, responding to the synchronization message to synchronize the time axis of the lidar with the time of the data processing device.

[0146] Preferably, before acquiring the raw measurement data from the lidar, the following steps are also performed:

[0147] The vehicle speed and body attitude parameters are acquired, and it is determined whether the preset dynamic calibration conditions are met based on the vehicle speed and body attitude parameters; wherein, the body attitude parameters include acceleration, steering wheel angle, yaw rate and pitch rate.

[0148] Preferably, the preset condition is:

[0149] The original measurement data is static and the confidence level is greater than a preset threshold.

[0150] Preferably, the process of fitting the lane line curve equation based on the lane line type and crossing points under high-precision positioning specifically includes:

[0151] High-precision positioning results are obtained in real time through a high-precision positioning module;

[0152] The high-precision positioning result is matched with a high-precision map to convert the real-world position into a position relative to the high-precision map.

[0153] The type and location of the lane lines matched based on the current location are obtained from the high-precision map broadcast. The lane line curve equation is obtained by fitting the curve with the vehicle center as the origin.

[0154] Preferably, the step of calculating the misalignment angle and misalignment displacement of the lidar based on the installation parameters of the lidar and the corrected azimuth and elevation angles specifically includes:

[0155] Based on the installation location parameters of the lidar, the corrected azimuth angle, and the corrected elevation angle, a system of equations is established:

[0156]

[0157] Among them, Range n θ represents the distance in the original measurement data. n This represents the azimuth angle in the original measurement data; θ represents the pitch angle in the original measurement data; θ0 represents the azimuth angle at the time of installation. ψ0 represents the pitch angle during installation; θ represents the roll angle during installation. Δ Indicates the azimuth angle to be calibrated; Indicates the pitch angle to be calibrated; ψ Δ Indicates the roll angle to be calibrated; (x Δ y Δ , z Δ ) represents the positional deviation to be calibrated; θ n ′ Indicates the corrected azimuth angle; Indicates the corrected pitch angle;

[0158] The misalignment angle of the lidar is obtained by solving the system of equations using an estimation method. and misalignment displacement (x) Δ y Δ , z Δ ).

[0159] Preferably, the calibration algorithm module further performs:

[0160] The vehicle attitude angle at time m is calculated as follows:

[0161]

[0162]

[0163]

[0164] Among them, YawAngle m PitchAngle m RollAngle m Let be the vehicle attitude angles at time m, yawRate, pitchRate, and rollRate be the yaw rate, pitch rate, and roll rate, respectively, and Δt be the update period for the vehicle attitude angles.

[0165] According to the formula:

[0166]

[0167]

[0168]

[0169] The misalignment angle of the lidar is filtered.

[0170] in, These are the filtered angles, and N is the number of accumulated frames. YawAngle is the misalignment angle calculated for the i-th frame.i PitchAngle i RokkAngle i Let be the vehicle attitude angles at time i.

[0171] Preferably, the calibration algorithm module further performs:

[0172] Calculate t i Time and t j The difference in vehicle attitude angle at each moment is:

[0173]

[0174]

[0175]

[0176] Among them, ΔYawAngle i-j For t i Time and t j The difference in yaw angle at any given moment, ΔPitchAngle i-j For t i Time and t j The difference in pitch angle at any given moment, ΔRollAngle i-j For t i Time and t j The difference in roll angle at any given moment, YawRate, PitchRate, and RollRate are the yaw rate, pitch rate, and roll rate, respectively;

[0177] A filtering algorithm is used to filter the misalignment angle of the lidar.

[0178] In specific implementation, the working principle, control process and technical effects of the lidar dynamic calibration device based on high-precision positioning provided in the embodiments of the present invention are the same as those of the lidar dynamic calibration method based on high-precision positioning in the above embodiments, and will not be repeated here.

[0179] Please see Figure 3 , Figure 3 This is a schematic diagram of another preferred embodiment of a lidar dynamic calibration device based on high-precision positioning provided by the present invention. The lidar dynamic calibration device based on high-precision positioning includes a processor 301, a memory 302, and a computer program stored in the memory 302 and configured to be executed by the processor 301. When the processor 301 executes the computer program, it implements the lidar dynamic calibration method based on high-precision positioning described in any of the above embodiments.

[0180] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory 302 and executed by the processor 301 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the high-precision positioning-based lidar dynamic calibration device.

[0181] The processor 301 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor 301 can be any conventional processor. The processor 301 is the control center of the high-precision positioning-based lidar dynamic calibration device, and connects the various parts of the high-precision positioning-based lidar dynamic calibration device through various interfaces and lines.

[0182] The memory 302 mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. Furthermore, the memory 302 can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart media card (SMC), a secure digital card (SD), and a flash card, or it can be other volatile solid-state storage devices.

[0183] It should be noted that the aforementioned high-precision positioning-based lidar dynamic calibration device may include, but is not limited to, processors and memory, as will be understood by those skilled in the art. Figure 3 The structural diagram is merely an example of the aforementioned dynamic calibration device for lidar based on high-precision positioning, and does not constitute a limitation on the aforementioned dynamic calibration device for lidar based on high-precision positioning. It may include more or fewer components than shown in the diagram, or combine certain components, or use different components.

[0184] This invention provides a method and apparatus for dynamic calibration of a lidar system based on high-precision positioning. The method involves acquiring raw lidar measurement data and filtering it according to preset conditions. Based on the lane line type and reference points under high-precision positioning, a lane line curve equation is fitted. The filtered lidar measurement data is then correlated with the lane line curve equation to associate the lidar measurements with the lane lines on a high-precision map. Based on this correlation, the azimuth and elevation angles calculated from the lane line curve equation are used to correct the azimuth and elevation angles in the raw measurement data. Finally, based on the lidar's installation parameters and the corrected azimuth and elevation angles, the lidar's misalignment angle and misalignment displacement are calculated. This invention obtains the lane line curve equation by fitting the lane line type and reference points under high-precision positioning, and uses the lane lines of structured roads as the true values ​​for lidar calibration, effectively improving the lidar calibration accuracy.

[0185] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0186] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A dynamic calibration method for lidar based on high-precision positioning, characterized in that, include: Acquire the raw measurement data from the lidar and filter it according to preset conditions; Based on the type and location of lane lines under high-precision positioning, the equation of the lane line curve is obtained by fitting. The filtered lidar measurement data is correlated with the lane line curve equation to link the lidar measurement values ​​with the lane lines under the high-precision map. Based on the above correlation, the azimuth and pitch angles calculated using the lane line curve equation are used to correct the azimuth and pitch angles in the original measurement data. Based on the installation parameters of the lidar and the corrected azimuth and elevation angles, the misalignment angle and misalignment displacement of the lidar are calculated.

2. The dynamic calibration method for lidar based on high-precision positioning as described in claim 1, characterized in that, Before acquiring the raw measurement data from the lidar, the process also includes: The lidar is synchronized with the high-precision positioning module in time.

3. The dynamic calibration method for lidar based on high-precision positioning as described in claim 2, characterized in that, Before acquiring the raw measurement data from the lidar, the process also includes: The vehicle speed and body attitude parameters are acquired, and it is determined whether the preset dynamic calibration conditions are met based on the vehicle speed and body attitude parameters; wherein, the body attitude parameters include acceleration, steering wheel angle, yaw rate and pitch rate.

4. The dynamic calibration method for lidar based on high-precision positioning as described in claim 1, characterized in that, The preset conditions are: The original measurement data is static and the confidence level is greater than a preset threshold.

5. The dynamic calibration method for lidar based on high-precision positioning as described in claim 1, characterized in that, The process of fitting the lane line curve equation based on the lane line type and crossing points under high-precision positioning specifically includes: High-precision positioning results are obtained in real time through a high-precision positioning module; The high-precision positioning result is matched with a high-precision map to convert the real-world position into a position relative to the high-precision map. The type and location of the lane lines matched based on the current location are obtained from the high-precision map broadcast. The lane line curve equation is obtained by fitting the curve with the vehicle center as the origin.

6. The dynamic calibration method for lidar based on high-precision positioning as described in claim 5, characterized in that, The calculation of the misalignment angle and misalignment displacement of the lidar based on its installation parameters and the corrected azimuth and elevation angles specifically includes: Based on the installation location parameters of the lidar, the corrected azimuth angle, and the corrected elevation angle, a system of equations is established: …… Among them, Range n θ represents the distance in the original measurement data. n This represents the azimuth angle in the original measurement data; θ represents the pitch angle in the original measurement data; θ0 represents the azimuth angle at the time of installation. ψ0 represents the pitch angle during installation; θ represents the roll angle during installation. Δ Indicates the azimuth angle to be calibrated; Indicates the pitch angle to be calibrated; ψ Δ Indicates the roll angle to be calibrated; (x Δ y Δ , z Δ ) represents the positional deviation to be calibrated; θ′ n Indicates the corrected azimuth angle; Indicates the corrected pitch angle; The misalignment angle of the lidar is obtained by solving the system of equations using an estimation method. and misalignment displacement (x) Δ y Δ , z Δ ).

7. The dynamic calibration method for lidar based on high-precision positioning as described in claim 6, characterized in that, The method further includes: The vehicle attitude angle at time m is calculated as follows: Among them, YawAngle m PitchAngle m RollAngle mm Let be the vehicle attitude angles at time m, yawRate, pitchRate, and rollRate be the yaw rate, pitch rate, and roll rate, respectively, and Δt be the update period for the vehicle attitude angles. According to the formula: The misalignment angle of the lidar is filtered. in, These are the filtered angles, and N is the number of accumulated frames. YawAngle is the misalignment angle calculated for the i-th frame. i PitchAngle i RollAngle i Let be the vehicle attitude angles at time i.

8. The dynamic calibration method for lidar based on high-precision positioning as described in claim 6, characterized in that, The method further includes: Calculate t i Time and t j The difference in vehicle attitude angle at each moment is: Among them, ΔYawAngle i-j For t i Time and t j The difference in yaw angle at any given moment, ΔPitchAngle i-j For t i Time and t j The difference in pitch angle at any given moment, ΔRollAngle i-j For t i Time and t j The difference in roll angle at any given moment, YawRate, PitchRate, and RollRate are the yaw rate, pitch rate, and roll rate, respectively; A filtering algorithm is used to filter the misalignment angle of the lidar.

9. A dynamic calibration device for lidar based on high-precision positioning, characterized in that, It includes a lidar and a data processing device, wherein the lidar and the data processing device are connected in communication. The lidar sends its raw measurement data to the data processing device; The data processing device is equipped with a calibration algorithm module, which performs the following processing during operation: The original measurement data is filtered according to preset conditions; based on the type and points of the lane lines under high-precision positioning, the lane line curve equation is obtained by fitting. The filtered lidar measurement data is correlated with the lane line curve equation to link the lidar measurement values ​​with the lane lines under the high-precision map. Based on the above correlation, the azimuth and pitch angles calculated using the lane line curve equation are used to correct the azimuth and pitch angles in the original measurement data. Based on the installation parameters of the lidar and the corrected azimuth and elevation angles, the misalignment angle and misalignment displacement of the lidar are calculated.

10. The dynamic calibration device for lidar based on high-precision positioning as described in claim 9, characterized in that, The data processing device is also equipped with a high-precision positioning module and a high-precision map. The high-precision positioning module is used to acquire high-precision positioning results in real time.

11. The dynamic calibration device for lidar based on high-precision positioning as described in claim 10, characterized in that, The data processing device acts as a time synchronization master, sending a synchronization message. The lidar acts as a time synchronization slave, responding to the synchronization message to synchronize the time axis of the lidar with the time of the data processing device.

12. A dynamic calibration device for lidar based on high-precision positioning, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the high-precision positioning-based dynamic calibration method for lidar as described in any one of claims 1 to 8.

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