LiDAR testing method based on long-range target ranging results

Abstract

The present invention discloses a laser radar testing method based on the distance measurement results of long-range targets, comprising the following steps: obtaining a laser radar echo signal and extracting the spectrum information of the echo signal; extracting Doppler frequency shift characteristic parameters based on the spectrum information and calculating the relative speed of the target based on a preset frequency shift solution model; obtaining multi-source sensor data associated with a laser radar test platform and fusing them to construct a dynamic distance measurement reference coordinate system; performing dynamic motion compensation on the laser ranging results based on the relative speed of the target to obtain a Doppler-corrected initial distance to the target; mapping the Doppler-corrected initial distance to the target into the dynamic distance measurement reference coordinate system to obtain a final distance measurement result of the long-range target aligned with the test platform coordinate system; and outputting the target's accurate position and movement status information in three-dimensional space based on the final distance measurement result. The present invention can improve ranging accuracy and stability.

Description

Technical Field

[0001] The present invention relates to the field of laser radar testing technology, and in particular to a laser radar testing method based on long-distance target ranging results. Background Art

[0002] LiDAR, a high-precision, non-contact ranging technology, has been widely used in recent years in areas such as autonomous driving, surveying and mapping, security monitoring, and intelligent transportation. Its basic principle is to transmit a laser beam, receive the target's reflected echo, and calculate the target's distance based on the laser's propagation time in the medium. However, in long-distance ranging scenarios, the laser signal intensity significantly attenuates, the target's echo signal is susceptible to environmental interference, and the relative motion of the target introduces additional error factors, resulting in insufficient stability and accuracy in ranging results.

[0003] Existing lidar testing methods mostly rely on static coordinate systems for measurement, ignoring the impact of the dynamic motion of the test platform itself on ranging results. This is particularly true in dynamic testing environments, such as those on vehicles and drones, where the relative motion between the laser and the target can significantly amplify ranging errors. Furthermore, when the target is in high-speed motion, the Doppler shift effect on the laser's reflected wave causes distortion in both the time and frequency domains, further impacting ranging accuracy.

[0004] At the same time, traditional ranging methods often rely on information from a single sensor, lack the fusion of multi-source data, and are unable to construct a complete and dynamically changing reference coordinate system in the real world. As a result, LiDAR testing accuracy in complex or changing environments cannot meet the requirements of high-precision applications. For example, on urban roads, in complex obstacle environments, or when the target is in violent motion, ranging results often fluctuate and jump, seriously affecting the calibration, performance verification, and stability analysis of LiDAR systems. Summary of the Invention

[0005] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a laser radar testing method based on long-distance target ranging results, comprising the following steps:

[0007] Acquire a laser radar echo signal and extract spectrum information of the echo signal;

[0008] The Doppler frequency shift characteristic parameters are extracted according to the spectrum information, and the relative speed of the target is calculated based on the preset frequency shift solution model. ;

[0009] Acquire multi-source sensor data associated with the LiDAR test platform and fuse them to construct a dynamic ranging reference coordinate system;

[0010] Performing dynamic motion compensation on the laser ranging result according to the relative speed of the target to obtain the initial measured distance of the target after Doppler correction;

[0011] Mapping the Doppler-corrected target initial range to the dynamic ranging reference coordinate system to obtain a final range measurement result of the long-range target aligned with the test platform coordinate system;

[0012] The accurate position and movement status information of the target in three-dimensional space are output according to the final ranging result.

[0013] As a preferred solution of the laser radar test method based on the long-distance target ranging result of the present invention, the frequency shift solution model is based on the laser wavelength Sum frequency shift value , the relative speed is calculated using the following relationship:

[0014] ,

[0015] in, is the relative speed of the target, and the obtained value is used for dynamic ranging compensation. is the Doppler frequency shift, c is the speed of light, is the emission frequency of the laser.

[0016] As a preferred solution of the laser radar testing method based on long-distance target ranging results described in the present invention, the multi-source sensor data includes but is not limited to IMU data, GPS data and visual sensor image data.

[0017] As a preferred solution of the laser radar test method based on the long-distance target ranging results described in the present invention, wherein: obtaining the multi-source sensor data associated with the laser radar test platform and fusing them to construct a dynamic ranging reference coordinate system includes the following steps:

[0018] Calculating attitude change information of the test platform according to the IMU data;

[0019] Acquire location information of the test platform according to the GPS data;

[0020] Constructing a visual SLAM image coordinate system of the platform's environment based on the visual sensor image data;

[0021] Based on the fusion of the IMU data, the GPS data and the visual SLAM image coordinate system data, a dynamic ranging reference coordinate system is constructed.

[0022] As a preferred solution of the laser radar testing method based on the long-distance target ranging results of the present invention, wherein: based on the fusion of the IMU data, the GPS data and the visual SLAM image coordinate system data, a dynamic ranging reference coordinate system is constructed, including the following steps:

[0023] The visual SLAM image coordinate system is used as the initial environment reference system, the attitude change derived by the IMU is used as the intermediate state constraint, and the position information of the test platform provided by the GPS is used as the global position factor to construct a fusion optimization model, which is a factor graph model;

[0024] A nonlinear optimization algorithm is used to globally optimize the above factor graph to obtain the optimal state estimation results of the platform at each moment, including the three-dimensional position coordinates (X, Y, Z) and the three-axis attitude angle;

[0025] The current platform state output by factor graph optimization is used as the coordinate system benchmark to construct a three-dimensional dynamic ranging reference coordinate system.

[0026] As a preferred embodiment of the laser radar testing method based on the long-distance target ranging result of the present invention, wherein: dynamic motion compensation is performed on the laser ranging result according to the relative speed of the target to obtain the initial measured distance of the target after Doppler correction, the method includes the following steps:

[0027] Get the raw lidar ranging results , and determine the data format and unit of the original ranging result;

[0028] The relative speed of the target is calculated , determine the time correction required to compensate ;

[0029] According to the compensation time correction amount , combined with the relative velocity of the target , calculate the ranging offset distance caused by target motion ;

[0030] Offset the distance Compared with the original ranging results Perform difference calculation to obtain the corrected distance after Doppler motion compensation , the specific calculation formula is: ;

[0031] Corrected distance after Doppler motion compensation Perform filtering and smoothing processing to output the initial target distance after Doppler correction.

[0032] As a preferred solution of the laser radar test method based on the long-distance target ranging result of the present invention, wherein: the relative speed of the target obtained by calculation , determine the time correction required to compensate , including the following steps:

[0033] According to the speed of light constant c, calculate the one-way propagation time of the laser signal , the propagation time Used to estimate the actual displacement of the target during the laser propagation period;

[0034] The relative speed of the target One-way propagation time Correlation, determining the target's displacement during signal propagation , the formula is: , the displacement Reflects the spatial offset of the target caused by its own motion during the round trip of the laser;

[0035] The displacement Convert to the corresponding time correction amount .

[0036] As a preferred embodiment of the laser radar test method based on the long-distance target ranging result of the present invention, the method includes mapping the Doppler-corrected initial target range to the dynamic ranging reference coordinate system to obtain the final long-distance target ranging result aligned with the test platform coordinate system, including the following steps:

[0037] Get the initial target distance after Doppler correction Data, and determine the data format and coordinate reference system attributes of the target's initial distance;

[0038] Obtain the dynamic ranging reference coordinate system information output by factor graph optimization at the current moment, including the platform's three-dimensional position and attitude angle;

[0039] According to the installation pose parameters of the lidar on the test platform, the pose transformation matrix between the local coordinate system of the original ranging data and the platform coordinate system is constructed;

[0040] The initial measured distance data of the target is converted into the three-dimensional position coordinates of the target in the local coordinate system of the test platform through the coordinate transformation matrix;

[0041] According to the current global state information of the test platform, the three-dimensional position coordinates of the target in the platform local coordinate system are mapped to the dynamic ranging reference coordinate system;

[0042] The three-dimensional coordinate values of the target that has completed spatial alignment are output as the final ranging result of the long-distance target aligned with the test platform coordinate system.

[0043] As a preferred embodiment of the laser radar testing method based on the long-distance target ranging result of the present invention, the method includes the following steps: outputting the accurate position and movement status information of the target in three-dimensional space according to the final ranging result:

[0044] Get the target 3D coordinates mapped to the dynamic ranging reference coordinate system ;

[0045] Combining the ranging results of multiple consecutive moments to construct the target space trajectory sequence;

[0046] Based on the target space trajectory sequence, the dynamic motion state information of the target is calculated and the real-time position and motion state of the target in three-dimensional space are output;

[0047] The dynamic motion state information includes a velocity vector, an acceleration vector, and a motion direction.

[0048] As a preferred solution of the laser radar testing method based on long-distance target ranging results described in the present invention, the calculation of the dynamic motion state information is based on the target ranging results at multiple consecutive moments, and the speed change trend and acceleration characteristics of the target are extracted through position difference method, Kalman filtering or other state estimation algorithms to reflect the target's motion behavior in real time.

[0049] Beneficial effects of the present invention:

[0050] 1. This invention introduces Doppler frequency shift feature extraction and frequency shift solution models to calculate the target's relative velocity and perform dynamic motion compensation on the laser ranging results, enabling the ranging process to adapt to ranging errors caused by high-speed target movement. Compared to traditional static or uniform velocity model ranging methods, this invention can effectively solve the ranging offset problem caused by high-speed targets and significantly improve ranging accuracy and stability in long-distance dynamic scenes.

[0051] 2. This invention integrates IMU, GPS, and visual SLAM image coordinate system data to construct a dynamic ranging reference coordinate system, enabling the laser ranging results to align with the spatial state changes of the test platform in real time. Compared with traditional methods that rely on fixed reference systems, this invention has higher environmental adaptability and robustness, and can effectively deal with coordinate system disturbances during complex dynamic processes such as movement, steering, and vibration of the test platform, ensuring the spatial consistency and temporal synchronization of ranging data.

[0052] 3. This invention not only outputs the position coordinates of distant targets in three-dimensional space but also calculates dynamic information such as the target's velocity and acceleration based on a multi-time ranging sequence, enabling behavioral analysis and trajectory tracking. This extensible capability enables the invention to expand into advanced application scenarios such as target recognition, motion prediction, and dynamic obstacle detection, significantly enhancing the practical value of lidar systems in intelligent transportation, dynamic monitoring, and environmental perception. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0054] Figure 1 This is a flowchart of the overall process of the laser radar testing method based on the long-distance target ranging results of the present invention. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0058] Furthermore, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, when describing the embodiments of the present invention, cross-sectional views illustrating device structures may be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0059] Example 1

[0060] Reference Figure 1, as one embodiment of the present invention, provides a laser radar testing method based on long-distance target ranging results, comprising the following steps:

[0061] S1: Obtain the lidar echo signal and extract the spectrum information of the echo signal.

[0062] Specifically, during the spectrum information extraction process, the echo signal is preprocessed, such as through denoising filtering and window function weighting, to improve the accuracy and stability of spectrum analysis. This method enables efficient extraction of spectral features from lidar echoes, laying the foundation for subsequent dynamic compensation and precise ranging in this application.

[0063] It should be noted that the echo signal is received by the lidar receiving system, forming the raw echo signal data. This echo signal contains information such as the distance, speed, and reflection intensity of the target object, which is the basis for subsequent distance calculation and speed estimation.

[0064] To further analyze the target's motion characteristics and improve ranging accuracy, the original echo signal undergoes spectrum extraction. Specifically, frequency domain analysis methods such as the Fast Fourier Transform (FFT) are used to process the echo signal, converting it from a time-domain signal to a frequency-domain signal, thereby obtaining spectrum information containing Doppler shift components. This spectrum information clearly characterizes the frequency variation characteristics contained in the echo signal, particularly the Doppler shift component caused by target motion. Analyzing characteristics such as the main peak position and energy distribution in the spectrum provides a foundation for subsequent Doppler shift extraction and target relative velocity calculation.

[0065] S2: Extract Doppler frequency shift characteristic parameters based on spectrum information and calculate the relative speed of the target based on the preset frequency shift solution model .

[0066] It should be noted that frequency shift characteristic parameters are extracted through spectrum analysis. Doppler frequency shift characteristic parameters include the main frequency offset, which is the Doppler frequency shift of the laser echo caused by the relative motion of the target. The Doppler frequency shift characteristic parameter reflects the radial velocity of the target relative to the lidar system and is key to achieving high-precision dynamic ranging and motion compensation.

[0067] Specifically, the frequency shift solution model is based on the laser wavelength Sum frequency shift value , the relative speed is calculated using the following relationship:

[0068] ,

[0069] in, is the relative speed of the target, and the obtained value is used for dynamic ranging compensation. is the Doppler frequency shift, c is the speed of light, is the emission frequency of the laser.

[0070] Through the above method, the present invention can accurately extract the Doppler characteristics of the target from the spectrum information, and calculate the real-time motion speed of the target relative to the lidar system through the frequency shift solution model, providing key data support for subsequent dynamic compensation and distance mapping.

[0071] S3: Acquire multi-source sensor data associated with the LiDAR test platform and fuse them to construct a dynamic ranging reference coordinate system to provide a unified spatial alignment basis for the LiDAR ranging results.

[0072] The multi-source sensor data includes, but is not limited to, inertial measurement unit (IMU) data, global positioning system (GPS) data, and vision sensor image data.

[0073] Specifically, acquiring multi-source sensor data associated with the lidar test platform and fusing them to construct a dynamic ranging reference coordinate system includes the following steps:

[0074] S31: Calculate the attitude change information of the test platform based on the IMU data.

[0075] It should be noted that the inertial measurement unit (IMU), including but not limited to gyroscopes and accelerometers, is used to measure the platform's three-axis angular velocity, acceleration, and other motion information in real time. By integrating and filtering IMU data and combining it with algorithms such as Kalman filtering or extended Kalman filtering, we can obtain information about the platform's attitude changes during ranging, primarily including pitch, roll, and yaw. This attitude change information serves as an important foundation for subsequent coordinate transformation and error compensation, helping to improve the accuracy of spatial alignment in laser ranging.

[0076] S32: Obtain the location information of the test platform according to the GPS data.

[0077] It should be noted that the Global Positioning System (GPS) can provide the platform's location information in a geographic coordinate system, with the typical data form being three-dimensional coordinates (longitude, latitude, and elevation).

[0078] Preferably, differential GPS or real-time kinematic positioning (RTK) technology is used to obtain high-precision position information. This high-precision position information provides a global position reference for constructing a ranging reference coordinate system and is used to constrain position variables in the factor graph.

[0079] S33: Construct a visual SLAM image coordinate system of the platform's environment based on the visual sensor image data.

[0080] It should be noted that image data collected by visual sensors (such as monocular or binocular cameras) can be used to construct a spatial model of the platform's environment. Preferably, simultaneous localization and mapping (SLAM) technology can be used to obtain a sparse or dense point cloud of the environment through feature point matching, depth estimation, and geometric recovery between image frames, thereby constructing a visual SLAM image coordinate system. This coordinate system, acting as an environmental reference system and in conjunction with IMU and GPS data, can constrain and compensate for drift errors, improving the robustness and stability of the overall system.

[0081] S34: Based on the fusion of IMU data, GPS data and visual SLAM image coordinate system data, a dynamic ranging reference coordinate system is constructed. Specifically, the following steps are included:

[0082] S34-1: Use the visual SLAM image coordinate system as the initial environment reference system, the attitude change derived by the IMU as the intermediate state constraint, and the position information of the test platform provided by the GPS as the global position factor to build a fusion optimization model.

[0083] Preferably, the fusion optimization model is a factor graph model, where nodes represent the platform's state (position + posture) at different times, and edges represent the constraints introduced by the sensor data. Optionally, a graph neural network structure is introduced to dynamically learn and optimize the weights between factors, improving fusion accuracy and robustness.

[0084] S34-2: Use a nonlinear optimization algorithm (such as g2o or Ceres Solver) to globally optimize the above factor graph, obtaining the optimal state estimate of the platform at each moment, including the 3D position coordinates (X, Y, Z) and the three-axis attitude angles (roll, pitch, and yaw). This optimization process effectively integrates multi-source data, offsetting their respective random and systematic errors.

[0085] S34-3: Using the current platform state output from factor graph optimization as the coordinate system reference, a 3D dynamic ranging reference coordinate system is constructed. The origin of the coordinates is defined as the platform's center of mass, with the X-axis oriented in the platform's forward direction, the Y-axis pointing to the left, and the Z-axis pointing vertically upward. This coordinate system updates in real time with platform motion and provides a unified spatial alignment reference for subsequent LiDAR ranging data.

[0086] S4: Perform dynamic motion compensation on the laser ranging result according to the relative speed of the target to obtain the initial measured distance of the target after Doppler correction.

[0087] Specifically, dynamic motion compensation is performed on the laser ranging result according to the relative speed of the target to obtain the initial measured distance of the target after Doppler correction, including the following steps:

[0088] S41: Get the original lidar ranging results , and determine the data format and unit of the original ranging results; ensure that the data is parsed correctly, the units are unified, the compensation logic is adapted, and the multi-source fusion is compatible, thus laying the foundation for subsequent dynamic motion compensation. In addition, the original lidar ranging results As the uncorrected initial distance value, it is the basis for calculating the target Doppler compensation.

[0089] S42: Determine the time correction required for compensation based on the relative speed of the target calculated in step S2 ;

[0090] Specifically, the relative speed of the target calculated in step S2 is , determine the time correction required to compensate , including the following steps:

[0091] S42-1: Calculate the one-way propagation time of the laser signal based on the speed of light constant c , the propagation time Used to estimate the actual displacement of the target during the laser propagation period;

[0092] S42-2: Change the target's relative speed One-way propagation time Correlation, determining the target's displacement during signal propagation , the formula is: , the displacement Reflects the spatial offset of the target caused by its own motion during the round trip of the laser;

[0093] S42-3: Displacement Convert to the corresponding time correction amount , preferably, by the formula Determine; optionally, if the target's relative speed Dynamic changes over time, the Perform correction calculation, where "+" is used when the target is far away from the radar and "-" is used when the target is close to the radar; the output As a dynamic compensation time parameter, it is used to correct the target's ranging offset.

[0094] S43: Correction amount based on compensation time , combined with the relative speed of the target , calculate the ranging offset distance caused by target motion , the formula is: ,; Ranging offset distance It represents the laser signal propagation path error caused by target motion and is a key parameter for subsequent corrections.

[0095] S44: offset the distance Compared with the original ranging results Perform difference calculation to obtain the corrected distance after Doppler motion compensation , the specific calculation formula is: The core of this step is to back-calculate the target's position between laser emission and reception, thereby significantly improving the ranging accuracy in dynamic target scenarios.

[0096] It should be noted that the corrected distance after Doppler motion compensation It corresponds one-to-one with the dynamic ranging reference coordinate system constructed in step S3 and is synchronized through timestamps to ensure accurate alignment of the coordinate mapping (S5).

[0097] S45: Corrected distance after Doppler motion compensation Perform filtering and smoothing processing to output the initial target distance after Doppler correction;

[0098] It should be noted that filtering and smoothing are performed to reduce accidental ranging fluctuations caused by relative speed estimation errors or system noise.

[0099] Preferably, a sliding average, Gaussian filter or Kalman filter algorithm can be used to achieve smooth output of distance data. Perform unified formatting to ensure data structure compatibility and consistency in the subsequent coordinate system mapping steps.

[0100] S5: Map the Doppler-corrected initial target range to the dynamic ranging reference coordinate system to obtain the final range measurement result of the long-range target aligned with the test platform coordinate system.

[0101] Specifically, the Doppler-corrected initial target range is mapped to the dynamic ranging reference coordinate system to obtain the final range measurement result of the long-range target aligned with the test platform coordinate system, including the following steps:

[0102] S51: Get the initial target distance after Doppler correction Data, and determine the data format and coordinate reference system attributes of the target's initial distance;

[0103] In this step, the initial target distance is corrected by the Doppler effect. The data reflects the relative distance change of the target. After Doppler correction, this data reflects the actual distance of the target relative to the measurement platform. To ensure the accuracy of data conversion, the data format and coordinate reference system properties of the initial target distance measurement must be determined. This includes analyzing the data type, accuracy, and definition of the spatial coordinate system to ensure compatibility and conversion with the test platform's coordinate system.

[0104] S52: Obtain the dynamic ranging reference coordinate system information output by the factor graph optimization at the current moment, including the platform's three-dimensional position and attitude angle.

[0105] In this step, obtaining the current dynamic ranging reference coordinate system information is crucial, specifically the platform's 3D position and attitude angles. The information output by the factor graph optimization algorithm provides the test platform's precise position information (i.e., the platform's 3D coordinates) and attitude angles (such as the platform's pitch, yaw, and roll angles). This information is the foundation for target position conversion and mapping, as the target's initial ranging measurement needs to be mapped into the test platform's coordinate system in subsequent steps.

[0106] S53: Constructing a pose transformation matrix between the local coordinate system to which the original ranging data belongs and the platform coordinate system according to the installation pose parameters of the laser radar on the test platform;

[0107] In this step, a coordinate transformation matrix is constructed based on the LiDAR's installation pose on the test platform. The LiDAR's installation pose parameters include the LiDAR's translation and rotation matrix relative to the test platform coordinate system. The translation represents the position of the LiDAR's mounting point in the platform coordinate system, while the rotation matrix represents the LiDAR's rotation relative to the platform coordinate system. These parameters facilitate the conversion from the LiDAR's local coordinate system to the test platform coordinate system, ensuring spatial alignment and accurate transformation of the data.

[0108] S54: Converting the initial measured distance data of the target into the three-dimensional position coordinates of the target in the local coordinate system of the test platform through the coordinate transformation matrix.

[0109] In this step, the initial measured distance of the target after Doppler correction is converted into Convert from the lidar's local coordinate system to the test platform's local coordinate system. This conversion is necessary because the lidar's ranging data is initially acquired relative to the lidar's own position. This step converts the target's 3D position coordinates to a local coordinate system relative to the test platform, providing a foundation for further calculations and coordinate mapping.

[0110] S55: Mapping the target three-dimensional position coordinates in the platform local coordinate system to the dynamic ranging reference coordinate system according to the current global state information of the test platform.

[0111] It's important to note that global state information includes factors such as the platform's dynamic position and attitude angle, ensuring that the target's position not only reflects its relative position to the platform but also accounts for the platform's dynamic changes. This allows for accurate positioning of the target within a changing coordinate reference system, ensuring the timeliness and accuracy of the ranging results.

[0112] S56: Output the three-dimensional coordinate value of the target that has completed spatial alignment as the final ranging result of the long-distance target aligned with the test platform coordinate system, which can be directly used for subsequent data analysis, target tracking and other applications.

[0113] In step S5, accurate ranging of distant targets can be ensured, ranging errors caused by the Doppler effect can be eliminated, and the consistency of the test platform coordinate system can be guaranteed through dynamic coordinate transformation, providing reliable data support for subsequent target tracking and positioning.

[0114] S6: Output the target's accurate position and movement status information in three-dimensional space based on the final ranging result.

[0115] The purpose of this step is to further extract the real-time position and dynamic motion state information of the target in three-dimensional space after completing the unified spatial alignment and Doppler motion compensation of the laser ranging data, so as to provide basic data support for subsequent target behavior analysis, navigation decision-making or early warning control.

[0116] Specifically, outputting the target's accurate position and movement status information in three-dimensional space based on the final ranging result includes the following steps:

[0117] S61: Obtain the target three-dimensional coordinate value mapped to the dynamic ranging reference coordinate system ;

[0118] It should be noted that the target three-dimensional coordinates refer to the spatial position coordinates of the target at the current moment after the laser radar ranging, dynamic compensation and coordinate transformation processing, in the form of a three-dimensional vector (X, Y, Z). By calling the dynamic ranging reference coordinate system constructed in step S3, the Doppler corrected distance result at the current moment can be The coordinate transformation is performed with the platform posture and position information to realize the mapping of the target position to a unified three-dimensional coordinate system, ensuring that all target data are under the same spatial reference and have good spatial consistency and comparability.

[0119] S62: Combining the ranging results of multiple consecutive moments to construct a target space trajectory sequence.

[0120] This step aims to mine the target's motion trajectory characteristics through time series information. Specifically, the system caches the ranging data for several consecutive time frames (for example, the past 1-2 seconds), extracts and records the target's 3D position coordinates at the corresponding time, and sequentially forms a target position trajectory sequence over continuous time, expressed as: , ,..., .in, is the target spatial position at the i-th moment. The trajectory sequence provides the input basis for the extraction of subsequent dynamic state quantities such as velocity and acceleration.

[0121] It should be noted that the trajectory sequence has a timestamp during the acquisition process, which supports differential calculation based on the time dimension and can optionally be combined with mechanisms such as sliding windows and recursive caching to optimize data processing efficiency.

[0122] S63: Based on the target spatial trajectory sequence, the target's dynamic motion state information is calculated and the target's real-time position and motion state in three-dimensional space are output to accurately characterize the target's motion behavior and its dynamic distribution characteristics in three-dimensional space.

[0123] The dynamic motion state information includes velocity vector, acceleration vector and motion direction.

[0124] Specifically, the calculation of motion state information is based on the target ranging results at multiple consecutive moments. Through position difference method, Kalman filter or other state estimation algorithms, the target's velocity change trend and acceleration characteristics are extracted to reflect the target's motion behavior in real time. The calculation method is as follows:

[0125] speed ,

[0126] in: and The spatial position coordinates of the target in the current frame and the previous frame, and is the corresponding timestamp, and the result is the three-dimensional velocity vector of the target at the current moment, in m / s.

[0127] acceleration ,

[0128] in: and is the velocity vector of the target in the current frame and the previous frame, in units of .

[0129] The acceleration vector reflects the changing trend of the target velocity and is used to determine whether the target is in an accelerating, decelerating or uniform motion state.

[0130] In summary, the present invention calculates the target relative speed by introducing Doppler shift feature extraction and frequency shift solution model, and performs dynamic motion compensation on the laser ranging results, so that the ranging process can adapt to the ranging error caused by the high-speed movement of the target. Compared with the ranging method of traditional static or uniform speed model, the present invention can effectively solve the ranging offset problem caused by high-speed targets, and significantly improve the ranging accuracy and stability in long-distance dynamic scenes. The present invention integrates IMU, GPS and visual SLAM image coordinate system data to construct a dynamic ranging reference coordinate system, so that the laser ranging results can be aligned with the spatial state changes of the test platform in real time. Compared with the traditional method that relies on a fixed reference system, the present invention has higher environmental adaptability and robustness, and can effectively deal with the coordinate system disturbance problem of the test platform in complex dynamic processes such as movement, steering, and vibration, ensuring the spatial consistency and time synchronization of the ranging data. The present invention not only outputs the position coordinates of the long-distance target in three-dimensional space, but also further calculates the target's speed, acceleration and other dynamic information based on the multi-time ranging sequence, which can realize the target's behavior analysis and trajectory tracking functions. This expansion capability gives the present invention the potential to expand into high-level application scenarios such as target recognition, motion prediction, and dynamic obstacle detection, significantly improving the practical value of lidar systems in intelligent transportation, dynamic monitoring, and environmental perception.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A laser radar testing method based on long-distance target ranging results, characterized in that: The following steps are involved: Acquire a laser radar echo signal and extract spectrum information of the echo signal; The Doppler frequency shift characteristic parameters are extracted according to the spectrum information, and the relative speed of the target is calculated based on the preset frequency shift solution model. ; Acquire multi-source sensor data associated with the LiDAR test platform and fuse them to construct a dynamic ranging reference coordinate system; Performing dynamic motion compensation on the laser ranging result according to the relative speed of the target to obtain the initial measured distance of the target after Doppler correction; Mapping the Doppler-corrected target initial range to the dynamic ranging reference coordinate system to obtain a final range measurement result of the long-range target aligned with the test platform coordinate system; Outputting the accurate position and movement status information of the target in three-dimensional space according to the final ranging result; Performing dynamic motion compensation on the laser ranging result according to the relative speed of the target to obtain the initial measured distance of the target after Doppler correction includes the following steps: Get the raw lidar ranging results , and determine the data format and unit of the original ranging results; The relative speed of the target is calculated , determine the time correction required to compensate ; Correction amount based on compensation time , combined with the relative velocity of the target , calculate the ranging offset distance caused by target motion ; Offset the distance Compared with the original ranging results Perform difference calculation to obtain the corrected distance after Doppler motion compensation , the specific calculation formula is: ; Corrected distance after Doppler motion compensation Perform filtering and smoothing processing to output the initial target distance after Doppler correction.

2. The laser radar testing method based on long-distance target ranging results according to claim 1, characterized in that: The frequency shift solution model is based on the laser wavelength Sum frequency shift value , the relative speed is calculated using the following relationship: in, is the relative speed of the target, and the obtained value is used for dynamic ranging compensation. is the Doppler frequency shift, is the speed of light, is the emission frequency of the laser.

3. The laser radar testing method based on long-distance target ranging results according to claim 1, characterized in that: The multi-source sensor data includes but is not limited to IMU data, GPS data, and visual sensor image data.

4. The laser radar testing method based on long-distance target ranging results according to claim 3, characterized in that: Acquiring the multi-source sensor data associated with the lidar test platform and fusing them to construct a dynamic ranging reference coordinate system includes the following steps: Calculating attitude change information of the test platform according to the IMU data; Acquire location information of the test platform according to the GPS data; Constructing a visual SLAM image coordinate system of the platform's environment based on the visual sensor image data; Based on the fusion of the IMU data, the GPS data and the visual SLAM image coordinate system data, a dynamic ranging reference coordinate system is constructed.

5. The laser radar testing method based on long-distance target ranging results according to claim 4, characterized in that: Based on the fusion of the IMU data, the GPS data and the visual SLAM image coordinate system data, a dynamic ranging reference coordinate system is constructed, comprising the following steps: The visual SLAM image coordinate system is used as the initial environment reference system, the posture change information derived by the IMU is used as the intermediate state constraint, and the position information of the test platform provided by the GPS is used as the global position factor to construct a fusion optimization model, wherein the fusion optimization model is a factor graph model; A nonlinear optimization algorithm is used to globally optimize the above factor graph to obtain the optimal state estimation results of the platform at each moment, including the three-dimensional position coordinates (X, Y, Z) and the three-axis attitude angle; The current platform state output by factor graph optimization is used as the coordinate system benchmark to obtain the three-dimensional dynamic ranging reference coordinate system.

6. The laser radar testing method based on long-distance target ranging results according to claim 1, characterized in that: According to the calculated relative speed of the target , determine the time correction required to compensate , including the following steps: According to the speed of light constant , calculate the one-way propagation time of the laser signal , the propagation time Used to estimate the actual displacement of the target during the laser propagation period; The relative speed of the target One-way propagation time Correlation, determining the target's displacement during signal propagation , the formula is: , the displacement Reflects the spatial offset of the target caused by its own motion during the round trip of the laser; The displacement Convert to the corresponding time correction amount .

7. The laser radar testing method based on long-distance target ranging results according to claim 1, characterized in that: Mapping the Doppler-corrected target initial range to the dynamic ranging reference coordinate system to obtain a final range measurement result of the long-range target aligned with the test platform coordinate system includes the following steps: Get the initial target distance after Doppler correction Data, and determine the data format and coordinate reference system attributes of the target's initial distance; Obtain the dynamic ranging reference coordinate system information output by factor graph optimization at the current moment, including the platform's three-dimensional position and attitude angle; According to the installation pose parameters of the lidar on the test platform, the pose transformation matrix between the local coordinate system of the original ranging data and the platform coordinate system is constructed; The initial measured distance data of the target is converted into the three-dimensional position coordinates of the target in the local coordinate system of the test platform through the coordinate transformation matrix; According to the current global state information of the test platform, the three-dimensional position coordinates of the target in the platform local coordinate system are mapped to the dynamic ranging reference coordinate system; The three-dimensional coordinate values of the target that has completed spatial alignment are output as the final ranging result of the long-distance target aligned with the test platform coordinate system.

8. The laser radar testing method based on long-distance target ranging results according to claim 7, characterized in that: Outputting the accurate position and movement status information of the target in three-dimensional space according to the final ranging result includes the following steps: Get the target 3D coordinates mapped to the dynamic ranging reference coordinate system ; Combining the ranging results of multiple consecutive moments to construct the target space trajectory sequence; Based on the target space trajectory sequence, the dynamic motion state information of the target is calculated and the real-time position and motion state of the target in three-dimensional space are output; The dynamic motion state information includes a velocity vector, an acceleration vector, and a motion direction.

9. The laser radar testing method based on long-distance target ranging results according to claim 8, characterized in that: The calculation of the dynamic motion state information is based on the target ranging results at multiple consecutive moments. The speed change trend and acceleration characteristics of the target are extracted through the position difference method and Kalman filter method to reflect the target's motion behavior in real time.

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