Motion compensation method for point cloud data and electronic device

Abstract

The application provides a motion compensation method for point cloud data, comprising: acquiring point cloud data, the point cloud data being formed by detecting a target object by using a laser radar and comprising a plurality of points, each point having spatial position information detected and an associated detection timestamp; quantizing the detection timestamps of the plurality of points at a preset time resolution to obtain a plurality of corresponding quantized timestamps; determining a unique timestamp set comprising at least one unique timestamp, the at least one unique timestamp being obtained by retaining only one of the same quantized timestamps in the plurality of quantized timestamps; determining pose change information related to each unique timestamp; and using the pose change information to perform motion compensation on the spatial position information detected at the detection timestamp corresponding to the related unique timestamp to obtain corrected spatial position information.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a motion compensation method, electronic device, and computer program product for point cloud data. Background Technology

[0002] Currently, LiDAR is increasingly being used in autonomous driving and various detection scenarios for precise measurement of the external environment. For example, in the field of autonomous driving, LiDAR uses lasers to scan and detect the vehicle's surroundings, quickly building a 3D model of the vehicle's surroundings to provide a basis for subsequent obstacle avoidance and autonomous driving strategies. LiDAR typically emits laser beams periodically and calculates the distance between the target object and the LiDAR by measuring the time it takes for the laser beam to return, thus detecting the surrounding environment. The data detected by LiDAR is usually represented as point cloud data. Point cloud data consists of a large number of discrete points, each representing a spatial location. These points not only provide information such as the object's geometry and distance, but can also be further calculated or analyzed to extract more features and attributes, such as orientation, normals, and color.

[0003] However, motion distortion is a common problem in LiDAR point clouds. The greater the relative velocity between the LiDAR and the target object, the more pronounced the deformation of the target object. This is because it takes time for the LiDAR to scan the entire scene, and the detection results returned by the LiDAR for the target object consist of discrete points distributed across multiple moments in time. If the relative position of the LiDAR to the outside world changes during detection, it will cause deformation of the target object (usually manifested as shape changes, multiple ghosting, etc.), which will seriously affect the accuracy of the detection results. Summary of the Invention

[0004] In view of this, the present disclosure provides motion compensation methods, electronic devices, and computer program products for point cloud data, which are intended to overcome some or all of the defects mentioned above, as well as other possible defects.

[0005] According to a first aspect of this disclosure, a motion compensation method for point cloud data is provided, comprising: acquiring point cloud data, the point cloud data being formed by detecting a target object using a lidar and including multiple temporally discrete points, each point having detected spatial location information and a detection timestamp associated with the detected spatial location information, the detection timestamp representing the time when the associated spatial location information was detected; quantizing the detection timestamps of the multiple points at a preset time resolution to obtain multiple corresponding quantized timestamps; determining a unique timestamp set, the unique timestamp set including at least one unique timestamp, the at least one unique timestamp being... A quantized timestamp is obtained by retaining only one of the same quantized timestamps from the plurality of quantized timestamps; pose change information associated with each of the at least one unique timestamp is determined, the pose change information characterizing the change in pose information of the lidar between the time represented by the unique timestamp and the time to which the detected spatial position information is to be compensated; using the pose change information, motion compensation is performed on the detected spatial position information at the corresponding detection timestamp to obtain corrected spatial position information associated with the corresponding detection timestamp, wherein the corresponding detection timestamp corresponds to the unique timestamp associated with the pose change information.

[0006] According to a second aspect of this disclosure, an electronic device is provided, comprising: a memory configured to store computer-executable instructions; and a processor configured to perform any of the methods described above when the computer-executable instructions are executed by the processor.

[0007] According to a third aspect of this disclosure, a computer program product is provided, characterized in that the computer program product includes computer-executable instructions that, when executed, implement any of the methods described above.

[0008] In the motion compensation method, electronic device, or computer program product for point cloud data claimed in this disclosure, after acquiring the point cloud data, the detection timestamps of the multiple points are first quantized to obtain multiple corresponding quantized timestamps. Then, a unique timestamp is determined based on the multiple corresponding quantized timestamps, and pose change information associated with each unique timestamp is determined. Finally, motion compensation is performed on the detected spatial position information at the detection timestamp corresponding to the associated unique timestamp using the pose change information. In this way, this solution only needs to determine the associated pose change information once for the unique timestamp, and then use the determined pose change information to perform motion compensation on the detected spatial position information at the detection timestamp associated with the associated unique timestamp, without iteratively calculating pose information and motion compensation for each point in the point cloud data. This greatly reduces the workload of determining pose change information and motion compensation, significantly improves the efficiency of motion compensation, and ensures the accuracy of the detection results.

[0009] These and other aspects of this application will become clearer from the embodiments described below, and will be elucidated with reference to the embodiments described below. Attached Figure Description

[0010] Embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings, in which:

[0011] Figure 1 The illustration shows an exemplary application scenario of using lidar to detect target objects in a scene;

[0012] Figure 2 An exemplary schematic diagram of motion compensation for point cloud data is shown;

[0013] Figure 3 An exemplary flowchart of a motion compensation method for point cloud data according to an embodiment of the present disclosure is illustrated;

[0014] Figure 4 The illustration shows an exemplary schematic diagram of motion compensation for point cloud data according to an embodiment of the present disclosure;

[0015] Figure 5 An exemplary structural block diagram of an exemplary electronic device is illustrated, which can be configured to implement a motion compensation method for point cloud data according to an embodiment of the present disclosure. Detailed Implementation

[0016] The following description provides specific details of various embodiments of this disclosure to enable those skilled in the art to fully understand and implement the various embodiments of this disclosure. It should be understood that the technical solutions of this disclosure can be implemented without some of these details. In some cases, this disclosure does not show or describe in detail some well-known structures or functions to avoid such unnecessary descriptions that would obscure the description of the embodiments of this disclosure. The terminology used in this disclosure should be understood in its broadest and most reasonable manner, even when used in conjunction with specific embodiments of this disclosure.

[0017] Figure 1 The illustration depicts an exemplary application scenario 100 of using lidar to detect target objects in a scene. For example... Figure 1 As shown, a lidar 101 is, for example, mounted on a vehicle 102 to detect target objects (here shown as small trees beside a road) 103 in a scene. The lidar 101 periodically emits laser beams to scan and detect the target objects, and the detection results are represented as point cloud data. When the vehicle is stationary, the target objects can be detected relatively accurately, and the detection results can be represented, for example, as point cloud data 104. When the vehicle is moving, the detection results can be represented, for example, as point cloud data 105. As can be seen from point cloud data 105, the detected target objects exhibit significant deformation, therefore motion compensation is required for the tested point cloud data.

[0018] The inventors discovered that current research on motion compensation for point cloud data mainly involves traversing each data point in the point cloud data and then performing motion compensation on each of the traversed data points one by one. Figure 2 An exemplary schematic diagram of motion compensation for point cloud data is shown. Figure 2 As shown, assume point cloud 105 contains n points, where n is a positive integer. Each point has spatial location information and a corresponding timestamp, indicating the time the point was detected. Due to the vehicle's motion, the coordinate system of the LiDAR at time ti (i is a positive integer) shifts compared to the initial time t0, causing deformation of the detected target object. Therefore, point position compensation is needed. Assuming we want to compensate the position of a point at time ti (three-dimensional spatial location information: x_i, y_i, z_i, timestamp: ti) to time t0, we need the pose information of the LiDAR or vehicle at times ti and t0. Pose information can be obtained, for example, directly from relevant sensors (e.g., inertial navigation systems) or calculated using position coordinates. Then, the pose information is used to compensate the spatial location information at time ti, thus obtaining the corrected position information. Figure 2 The image shows the result of applying motion compensation to point (x_1, y_1, z_1; t1), resulting in point (x*_1, y*_1, z*_1; t0). The result of applying motion compensation to all points can be shown as follows: Figure 2 As shown in 106. However, the inventors found that iterative calculation of pose information and motion compensation is required for each point. However, with the iteration and price reduction of LiDAR, the number of point clouds and the number of data points in the same scene are constantly increasing, and the time and processing resources spent on motion compensation are also increasing rapidly.

[0019] Figure 3 An exemplary flowchart of a motion compensation method 300 for point cloud data according to an embodiment of the present disclosure is illustrated. As an example, it will be discussed below in conjunction with... Figure 4 The exemplary schematic diagram shown, illustrating motion compensation of point cloud data according to an embodiment of this disclosure, describes the motion compensation method 300 for point cloud data. For example... Figure 3 As shown, the method 300 includes the following steps.

[0020] In step S310, point cloud data is acquired. This point cloud data is formed by detecting a target object using a lidar system and includes multiple time-discrete points. Each point has spatial location information of the detected point and a detection timestamp associated with that spatial location information. The detection timestamp indicates the time when the associated spatial location information was detected. Typically, a lidar system periodically emits a laser beam to detect target objects in the surrounding environment. The detected target objects can be represented by point cloud data, which includes multiple time-discrete points, such as... Figure 4 The point cloud data is shown in image 401. This point cloud data can be formed from one or more frames of image data detected by a LiDAR within a predetermined time period. The predetermined time period can be set based on experience or requirements, and is typically the duration of the LiDAR's operating cycle. Each point has detected spatial location information. This spatial location information is typically three-dimensional spatial coordinates in the LiDAR coordinate system, such as (x_i, y_i, z_i). Each point also has a detection timestamp associated with its detected spatial location information. The detection timestamps of the multiple points are, for example, as shown below. Figure 4 List 402 shows n detection timestamps t1, t2, ..., tn, where n is a positive integer and equal to the number of points in the point cloud. After being detected by the lidar, the point cloud data can be stored in a local or cloud-based database. During motion compensation, the point cloud data can be retrieved from the local or motion database.

[0021] In step S320, the detection timestamps of the multiple points are quantized at a preset time resolution to obtain multiple corresponding quantized timestamps. The preset time resolution can be set as needed. For example, the scanning cycle of a typical laser radar is 100ms, so the time resolution can be set to 0.01ms. Thus, assuming the point cloud detected during the scanning cycle includes 200,000 points, the detection timestamps of these points will be quantized into one of 10,000 quantized timestamps. Figure 4 As shown, detection timestamps t1, t3, t9, and t10 are quantized into quantized timestamp T1, and detection timestamps t4 and t8 are quantized into quantized timestamp T3, as shown in List 403. In some embodiments, the detection timestamps of the multiple points are quantized in parallel at a preset time resolution to obtain multiple corresponding quantized timestamps, which can improve the efficiency of quantization. The preset time resolution can be determined as needed, typically depending on requirements for error and time consumption.

[0022] In step S330, a unique timestamp set is determined, comprising at least one unique timestamp, which is a quantized timestamp obtained by retaining only one of the identical quantized timestamps among the plurality of corresponding quantized timestamps. As an example, the unique timestamp set can be obtained using a unique function (e.g., a function in C++ or Python) that removes duplicate elements from an array or list. Figure 4 In the table, by applying the unique operator function to the quantized timestamps in list 403, a unique timestamp set as shown in list 404 can be obtained, which includes quantized timestamps T1, T2, T3, T4, and T5, each with a quantity of 1.

[0023] In step S340, pose change information associated with each of the at least one unique timestamp is determined. This pose change information characterizes the change in pose information of the lidar between the time represented by the unique timestamp and the time to which the detected spatial location information is to be compensated. The pose information here can be, for example, the orientation and position coordinates of the lidar or the vehicle in a reference coordinate system (e.g., a geodetic or world coordinate system). The reference coordinate system is a coordinate system that does not change with the movement of the vehicle or lidar. The pose information can be obtained from other sensing devices, such as inertial navigation devices or GPS devices, and is not limited here. The time to be compensated can be preset, and is typically the smallest detection timestamp among multiple temporally discrete points in the point cloud data (i.e., ...). Figure 4(At time t1 in the context of lidar detection). Since the lidar detection results at time t1 are more accurate than at subsequent times, setting the compensation time to time t1 may be beneficial for improving the accuracy of the compensated results. Of course, this is not a limitation; compensating to the initial lidar detection time t0 or the time represented by the quantization timestamp is also feasible.

[0024] In some embodiments, when determining pose change information associated with each unique timestamp among the at least one unique timestamps, a first matrix and a second matrix may be determined first, wherein the first matrix represents the pose information of the lidar at the time represented by each unique timestamp, and the second matrix represents the pose information of the lidar at the time to which the detected spatial position information is to be compensated; then, the third matrix obtained by multiplying the inverse of the second matrix by the first matrix is ​​determined as the pose change information.

[0025] As an example, suppose the first matrix is ​​W1, which represents the pose information of the lidar at the time indicated by the unique timestamp T1; and the second matrix is ​​W2, which represents the pose information of the lidar at the time to which the detected spatial position information is to be compensated (e.g., t1 as described above). W1 and W2 can be in the form of 4*4 matrices as follows:

[0026]

[0027] Where, r 11 r 12 ... r 33 The orientation information in the pose information is represented by tx, ty, and tz, which are the three-dimensional position coordinates in the pose information. In this case, the pose change information Z1 associated with the unique timestamp T1 can be represented as W2. -1 *W1 represents the change in pose information between time T1 and time t1, and is still in matrix form. Pose change information associated with other unique timestamps can be determined in a similar manner. This provides an efficient and accurate way to determine pose change information, but it is not a limiting factor.

[0028] In step S350, motion compensation is performed on the detected spatial position information at the corresponding detection timestamp using the pose change information to obtain corrected spatial position information associated with the corresponding detection timestamp, wherein the corresponding detection timestamp corresponds to a unique timestamp related to the pose change information. Here, the number of corresponding detection timestamps is typically multiple, but not limited. Motion compensation can be performed on all or part of the detected spatial position information at the multiple corresponding detection timestamps corresponding to the associated unique timestamp. (Refer to...) Figure 4Continuing with the example above, after determining the pose change information Z1 associated with the unique timestamp T1, the detection timestamps corresponding to the unique timestamp T1 can be determined as t1, t3, t9, and t10, as shown in Listing 405. The pose change information Z1 is then used to perform motion compensation on the detected spatial position information at detection timestamps t1, t3, t9, and t10 to obtain corrected spatial position information associated with those timestamps. Similarly, pose change information associated with other unique timestamps can be used to perform motion compensation on the detected spatial position information at the corresponding detection timestamps. The motion-compensated point cloud can be as follows: Figure 4 The point cloud is shown in Figure 406.

[0029] As described above, the pose change information is represented by a pose change matrix. In this case, when performing motion compensation on the spatial position information detected at the corresponding detection timestamp, the pose change matrix can be multiplied by the spatial position information detected at the corresponding detection timestamp to obtain the corrected spatial position information associated with the corresponding detection timestamp.

[0030] In some embodiments, the plurality of corresponding quantized timestamps constitute elements of a first data set, that is, the plurality of corresponding quantized timestamps are stored in the first data set. In this case, when performing motion compensation on the spatial location information detected at the corresponding probe timestamp corresponding to the associated unique timestamp, a mask set corresponding to the associated unique timestamp can be determined first, wherein the elements in the mask set correspond to the elements in the first data set, and the value of each element in the mask set indicates whether the corresponding quantized timestamp in the first data set is the same as the associated unique timestamp; then, based on the elements in the mask set, at least one quantized timestamp in the first data set that is the same as the associated unique timestamp is determined; next, from the probe timestamps and detected spatial location information of the points in the point cloud data, the corresponding probe timestamp corresponding to the acquired at least one quantized timestamp, and the detected spatial location information associated with the corresponding probe timestamp are obtained; finally, motion compensation is performed on the detected spatial location information using the pose change information. In this way, by using a mask set to determine at least one quantized timestamp that is the same as the associated unique timestamp, and accordingly obtaining the corresponding probe timestamp and the probed spatial location information associated with the corresponding probe timestamp, parallel acquisition of probe timestamp and spatial location information can be achieved, greatly improving the processing efficiency of the entire process.

[0031] As an example, refer to Figure 4The multiple quantized timestamps in list 403 constitute the first data set S1. When performing motion compensation on the detected spatial location information at detection timestamps t1, t3, t9, and t10 corresponding to the unique timestamp T1, a mask set M1 corresponding to the unique timestamp T1 can be determined first. The elements in mask set M1 correspond to the elements in the first data set S1, and the number of elements is the same. The value of each element in mask set M1 is typically a Boolean value; a value of 1 indicates that the corresponding quantized timestamp in S1 is the same as T1, and a value of 0 indicates that the corresponding quantized timestamp in S1 is different from T1. Then, based on the elements in mask set M1, at least one quantized timestamp in the first data set that is the same as the associated unique timestamp is determined. For example, Figure 4 The four timestamps t1, t3, t9, and t10 are respectively associated with the detection timestamps. Next, from the detection timestamps and detected spatial location information of the points in the point cloud data, the corresponding detection timestamps corresponding to at least one acquired quantized timestamp, and the detected spatial location information associated with the corresponding detection timestamps, are obtained. For example, detection timestamps t1, t3, t9, and t10, and the detected spatial location information associated with each of the detection timestamps t1, t3, t9, and t10, can be obtained. Finally, motion compensation is performed on the detected spatial location information using the pose change information.

[0032] In the motion compensation method for point cloud data claimed in this disclosure, after acquiring the point cloud data, the detection timestamps of the multiple points are first quantized to obtain multiple corresponding quantized timestamps. Then, a unique timestamp is determined based on the multiple corresponding quantized timestamps, and pose change information associated with each unique timestamp is determined. Finally, motion compensation is performed on the detected spatial position information at the corresponding detection timestamp associated with the associated unique timestamp using the pose change information. In this way, this solution only needs to determine the associated pose change information once for the unique timestamp, and then use the determined pose change information to perform motion compensation on the detected spatial position information at the detection timestamp associated with the associated unique timestamp, without iteratively calculating pose information and motion compensation for each point in the point cloud data. This greatly reduces the workload of determining pose change information and motion compensation, significantly improves the efficiency of motion compensation, and ensures the accuracy of the detection results.

[0033] As an example, assuming the point cloud data contains 200,000 points, and all points are distributed within a time interval of 0ms to 100ms (the typical duty cycle of a LiDAR is 100ms), and the preset time resolution during quantization is set to 0.01ms, then the maximum number of unique timestamps obtained after quantization is 100ms / 0.01ms = 10,000. Therefore, this scheme only needs to acquire pose change information a maximum of 10,000 times to perform motion compensation, instead of requiring 200,000 iterations to obtain the motion compensation result. Moreover, test results show that even when the vehicle carrying the LiDAR is traveling at a speed of 200km / h, it only introduces a quantization error of approximately 0.36cm, greatly improving the efficiency of motion compensation and ensuring the accuracy of the detection results.

[0034] Figure 5 An exemplary structural block diagram of an exemplary electronic device 500 is illustrated. The electronic device 500 can be configured to implement a motion compensation method for point cloud data according to an embodiment of the present disclosure.

[0035] Electronic device 500 includes one or more processors or processing units 510, one or more memories or storage devices 520, one or more input / output (I / O) devices 530, and a bus 540 that allows various components and devices to communicate with each other. The one or more memories or storage devices 520 and the one or more input / output (I / O) devices 530 may be part of electronic device 500, or alternatively coupled to electronic device 500. Bus 540 represents one or more types of bus architectures, including memory buses or memory controllers using various different bus architectures, peripheral buses, accelerated graphics ports, processor or local buses, etc. Bus 540 may include wired and / or wireless buses.

[0036] The memory or storage device 520 may include volatile storage media (such as random access memory (RAM)) and / or non-volatile media (such as read-only memory (ROM), flash memory, optical disk, magnetic disk, etc.).

[0037] The techniques discussed herein can be implemented as software having instructions executable by one or more processors or processing units 510. It should be understood that different instructions can be stored in different components of the electronic device 500, such as in the processor or processing unit 510, in various cache memories of the processor or processing unit 510, in other cache memories of the electronic device 500 (not shown), in memory or storage devices 520, etc.

[0038] One or more input / output (I / O) devices 530 allow commands and information to be input into electronic device 500, and also allow information to be presented to a user and / or other devices. Examples of input devices include keyboards, cursor control devices (e.g., mice), microphones, scanners, data receivers, and so on. Examples of output devices include display devices (e.g., monitors or projectors), speakers, printers, network interface cards (NICs), and so on.

[0039] Here, various technologies are described in the general context of software or program modules. Typically, software includes routines, programs, objects, data structures, etc., that perform specific tasks or implement specific abstract data types. Implementations of these modules and technologies may be stored in or transferred across some form of computer-readable storage medium. Computer-readable storage media can be any available medium accessible to an electronic device.

[0040] "Computer-readable storage media" includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, RAM, ROM, flash memory or other memory technologies, CD-ROM, or other optical storage devices, magnetic tape, disk storage devices or other magnetic storage devices, or any other media that can be used to store desired information and can be accessed by a computer.

[0041] The technical solution disclosed herein can be implemented in the electronic device 500. The electronic device may include a lidar system or a domain controller, etc. A domain controller is an integrated electronic control unit that has emerged with the development of automotive electronics and intelligence, improving the vehicle's intelligence level by integrating the functions of multiple electronic control units (ECUs). The domain controller can be implemented as an autonomous driving domain controller.

[0042] This disclosure also provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of an electronic device or computing device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device or computing device to perform any of the methods provided in the various optional implementations described above.

[0043] By studying the accompanying drawings, the disclosure, and the appended claims, those skilled in the art can understand and implement variations of the disclosed embodiments in practicing the claimed subject matter. In the claims, the word "comprising" does not exclude other elements or steps, and "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used advantageously.

Claims

1. A motion compensation method for point cloud data, comprising: Point cloud data is acquired, which is formed by using lidar to detect the target object and includes multiple time-discrete points. Each point has the spatial location information of the detected point and a detection timestamp associated with the spatial location information. The detection timestamp indicates the time when the associated spatial location information was detected. The detection timestamps of the multiple points are quantized at a preset time resolution to obtain multiple corresponding quantized timestamps; Determine a unique timestamp set, the unique timestamp set including at least one unique timestamp, the at least one unique timestamp being a quantized timestamp obtained by retaining only one of the same quantized timestamps among the plurality of corresponding quantized timestamps; Determine pose change information associated with each of the at least one unique timestamps, the pose change information characterizing the change in pose information of the lidar between the time represented by the unique timestamp and the time to which the detected spatial location information is to be compensated. Using the pose change information, motion compensation is performed on the detected spatial position information at the corresponding detection timestamp to obtain corrected spatial position information associated with the corresponding detection timestamp, wherein the corresponding detection timestamp corresponds to a unique timestamp associated with the pose change information.

2. The method according to claim 1, wherein, The time to which the detected spatial location information is compensated is the smallest detection timestamp among the detection timestamps of the plurality of time-discrete points or the initial time when the lidar begins detection.

3. The method according to claim 1, wherein, The determination of pose change information associated with each unique timestamp among the at least one unique timestamp includes: Determine a first matrix and a second matrix, wherein the first matrix represents the pose information of the lidar at the time represented by each unique timestamp, and the second matrix represents the pose information of the lidar at the time to which the detected spatial position information is to be compensated; The third matrix obtained by multiplying the inverse of the second matrix by the first matrix is ​​determined as the pose change information.

4. The method according to claim 1, wherein, The plurality of corresponding quantized timestamps constitute elements of the first data set, and wherein, using the pose change information, motion compensation is performed on the detected spatial position information at the corresponding detection timestamp, including: Determine a mask set corresponding to the associated unique timestamp, wherein the elements in the mask set correspond to the elements in the first data set, and the value of each element in the mask set indicates whether the corresponding quantized timestamp in the first data set is the same as the associated unique timestamp; Based on the elements in the mask set, determine at least one quantized timestamp in the first data set that is identical to the associated unique timestamp; From the detection timestamps and detected spatial location information of the points in the point cloud data, obtain the corresponding detection timestamp corresponding to at least one quantized timestamp, and the detected spatial location information associated with the corresponding detection timestamp; The pose change information is used to perform motion compensation on the detected spatial position information.

5. The method according to claim 1, wherein, The pose change information is represented by a pose change matrix, and the motion compensation for the detected spatial position information at the corresponding detection timestamp includes: The pose change matrix is ​​multiplied by the spatial location information detected at the corresponding detection timestamp corresponding to the associated unique timestamp to obtain the corrected spatial location information associated with the corresponding detection timestamp.

6. The method according to claim 1, wherein, The step of quantizing the detection timestamps of the multiple points at a preset time resolution to obtain multiple corresponding quantized timestamps includes: The detection timestamps of the multiple points are quantized in parallel at a preset time resolution to obtain multiple corresponding quantized timestamps.

7. An electronic device, comprising Memory, which is configured to store computer-executable instructions; A processor configured to perform the method as described in any one of claims 1-6 when the computer-executable instructions are executed by the processor.

8. The electronic device of claim 7, wherein the electronic device comprises a lidar or a domain controller.

9. A computer program product, characterized in that, The computer program product includes computer-executable instructions that, when executed, implement the method according to any one of claims 1-6.

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