Point cloud data processing method and device, electronic equipment and storage medium

Abstract

The invention provides a point cloud data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a point cloud data block in a current collection frame of a laser radar and measurement data of an inertial measurement unit, the point cloud data block being obtained by dividing laser points collected by the laser radar in advance, adjusting each point cloud data block to a frame head position of the current acquisition frame, generating a rotation transformation quantity and a translation transformation quantity of each point cloud data block by using the measurement data, determining the point cloud data block corresponding to the laser point, and performing the same motion compensation on the laser point by using the rotation transformation quantity and the translation transformation quantity of the point cloud data block to obtain a motion compensation result; and obtaining compensated target point cloud data. According to the method, one frame of laser radar data is divided into a plurality of data blocks, the rotation transformation quantity and the translation transformation quantity of the point cloud data blocks are calculated, the same rotation transformation and translation transformation are directly executed on the laser points on each point cloud data block, and efficient laser radar point cloud motion distortion compensation is achieved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of autonomous driving technology, and in particular to a point cloud data processing method, device, electronic device and storage medium. Background Art

[0002] LiDAR is a high-precision active sensor that is gradually being widely used in high-level autonomous driving solutions. When LiDAR is mounted on an autonomous driving vehicle, the scanning imaging principle of the LiDAR sensor is a slow exposure process, and the vehicle itself is moving relative to the surrounding environment. As a result, during the scanning process of the same frame of data, the LiDAR's own coordinate system will change with the movement of the vehicle, resulting in motion distortion of the point cloud data, which cannot truly reflect the three-dimensional environment around the autonomous driving vehicle at a certain moment.

[0003] In order to obtain accurate point cloud data, it is necessary to compensate for motion distortion. The current common TOF (Time-of-Flight) lidar point cloud distortion compensation method is to estimate the movement of the lidar during the scanning process and transform each point cloud data accordingly to eliminate the distortion caused by the motion. The main method is to interpolate the coordinates of the lidar point cloud point by point according to the posture state of the first and last frames. However, due to the large amount of lidar point cloud data, point-by-point linear interpolation has the problems of large amount of calculation and low efficiency, which further affects the motion distortion compensation effect of the lidar point cloud data. Summary of the invention

[0004] One of the purposes of the present invention is to provide a point cloud data processing method to solve the problem of large computational complexity and low efficiency in point-by-point linear interpolation processing of a large amount of point cloud data, so as to achieve efficient point cloud data processing; the second purpose is to provide a point cloud data processing device; the third purpose is to provide an electronic device; the fourth purpose is to provide a storage medium.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A first aspect of an embodiment of the present invention provides a point cloud data processing method, the method comprising:

[0007] Obtaining a point cloud data block in a current acquisition frame of the laser radar and measurement data of an inertial measurement unit; wherein the point cloud data block is obtained by pre-dividing the laser points collected by the laser radar;

[0008] Adjusting each point cloud data block to the frame head position of the current acquisition frame, and using the measurement data to generate the rotational transformation amount and the translational transformation amount of each point cloud data block;

[0009] The point cloud data block corresponding to the laser point is determined, and the rotation transformation amount and the translation transformation amount of the point cloud data block are used to perform the same motion compensation on the laser point to obtain compensated target point cloud data.

[0010] Optionally, the step of acquiring a point cloud data block of a current acquisition frame of the laser radar and measurement data of an inertial measurement unit is performed; wherein the point cloud data block is obtained by pre-dividing laser points acquired by the laser radar, and includes:

[0011] Pre-synchronize the time of the LiDAR and the IMU;

[0012] Obtaining laser points collected by the laser radar in the current acquisition frame, dividing the laser points into data blocks using a preset number of message packets, and obtaining point cloud data blocks of the current acquisition frame; wherein the number of the point cloud data blocks is the same as the preset number of message packets;

[0013] Get the time-synchronized point cloud data blocks of the LiDAR and the measurement data of the inertial measurement unit.

[0014] Optionally, the step of adjusting each point cloud data block to the frame head position of the current acquisition frame and using the measurement data to generate a rotational transformation amount and a translational transformation amount of each point cloud data block includes:

[0015] Determine the target measurement data at the first moment of the current acquisition frame, and use the target measurement data to adjust the point cloud data block to the first moment of the frame;

[0016] Obtaining ordered timestamps of the point cloud data block, using the ordered timestamps to determine the measurement data at the first moment and the second moment, and obtaining the rotation quaternion change and the translation change from the first moment to the second moment; wherein the ordered timestamp is between the first moment and the second moment;

[0017] The rotational quaternion change and the translational change from the first moment to the second moment are used to generate the rotational transformation amount and the translational transformation amount of the point cloud data block.

[0018] Optionally, determining the target measurement data at the first moment of the current acquisition frame, and adjusting the point cloud data block to the first moment of the frame using the target measurement data, includes:

[0019] Acquire the first measurement data before the beginning moment of the current acquisition frame and the second measurement data after the end moment of the current acquisition frame;

[0020] Using the first measurement data and the second measurement data, determine the target measurement data at the first moment of the frame; wherein the target measurement data includes a rotation quaternion and three-axis coordinates;

[0021] The point cloud data block is adjusted to the first frame moment using the rotation quaternion and the three-axis coordinates.

[0022] Optionally, the step of using the rotation quaternion variation and the translation variation from the first moment to the second moment to generate the rotation transformation amount and the translation transformation amount of the point cloud data block includes:

[0023] Using the rotation quaternion change and the translation change from the first moment to the second moment, interpolation parameters of the ordered timestamps from the first moment to the point cloud data block are obtained;

[0024] The rotational quaternion variation, the translational variation and the interpolation parameters are used to generate the rotational transformation amount and the translational transformation amount of the point cloud data block.

[0025] Optionally, the determining the point cloud data block corresponding to the laser point, and using the rotational transformation amount and the translational transformation amount of the point cloud data block to perform motion compensation on the laser point to obtain compensated target point cloud data includes:

[0026] Determine the serial number of the laser point and the number of laser points in the point cloud data block;

[0027] According to the serial number of the laser point and the number of laser points in the point cloud data block, determine the point cloud data block to which the laser point belongs;

[0028] The laser point is subjected to rotational transformation using the rotational transformation amount of the point cloud data block, and the laser point subjected to rotational transformation is subjected to translational transformation using the translational transformation amount of the point cloud data block, so as to obtain compensated target point cloud data.

[0029] A second aspect of an embodiment of the present invention provides a point cloud data processing device, the device comprising:

[0030] A data acquisition module is used to acquire a point cloud data block in a current acquisition frame of the laser radar and measurement data of an inertial measurement unit; wherein the point cloud data block is obtained by pre-dividing the laser points collected by the laser radar;

[0031] A compensation amount generation module is used to adjust each point cloud data block to the frame head position of the current acquisition frame, and use the measurement data to generate the rotational transformation amount and the translational transformation amount of each point cloud data block;

[0032] The data compensation module is used to determine the point cloud data block corresponding to the laser point, and use the rotation transformation amount and translation transformation amount of the point cloud data block to perform the same motion compensation on the laser point to obtain compensated target point cloud data.

[0033] Optionally, the data acquisition module includes:

[0034] A synchronization submodule, used to pre-synchronize the laser radar and the inertial measurement unit;

[0035] A division submodule is used to obtain the laser points collected by the laser radar in the current acquisition frame, divide the laser points into data blocks using a preset number of message packets, and obtain point cloud data blocks of the current acquisition frame; wherein the number of the point cloud data blocks is the same as the preset number of message packets;

[0036] The acquisition submodule is used to obtain the point cloud data blocks of the laser radar after time synchronization and the measurement data of the inertial measurement unit.

[0037] Optionally, the compensation amount generating module includes:

[0038] An adjustment submodule, used to determine the target measurement data at the first moment of the current acquisition frame, and use the target measurement data to adjust the point cloud data block to the first moment of the frame;

[0039] A first determination submodule is used to obtain an ordered timestamp of a point cloud data block, and use the ordered timestamp to determine the measurement data of a first moment and a second moment, so as to obtain a rotation quaternion change and a translation change from the first moment to the second moment; wherein the ordered timestamp is between the first moment and the second moment;

[0040] The generating submodule is used to generate the rotational transformation amount and the translational transformation amount of the point cloud data block by using the rotational quaternion change amount and the translational change amount from the first moment to the second moment.

[0041] Optionally, the adjustment submodule includes:

[0042] An acquisition unit, used to acquire first measurement data before the beginning moment of a current acquisition frame and second measurement data after the end moment of the current acquisition frame;

[0043] A determination unit, configured to determine target measurement data at the frame start time by using the first measurement data and the second measurement data; wherein the target measurement data includes a rotation quaternion and three-axis coordinates;

[0044] The adjustment unit is used to adjust the point cloud data block to the first frame moment by using the rotation quaternion and the three-axis coordinates.

[0045] Optionally, the generating submodule includes:

[0046] A calculation unit, used to obtain interpolation parameters of ordered timestamps from the first moment to the point cloud data block by using the rotation quaternion change and the translation change from the first moment to the second moment;

[0047] The generating unit is used to generate the rotational transformation amount and the translational transformation amount of the point cloud data block by using the rotational quaternion variation amount, the translational variation amount and the interpolation parameter.

[0048] Optionally, the data compensation module includes:

[0049] The second determination submodule is used to determine the serial number of the laser point and the number of laser points in the point cloud data block;

[0050] A third determination submodule is used to determine the point cloud data block to which the laser point belongs according to the sequence number of the laser point and the number of laser points in the point cloud data block;

[0051] The transformation submodule is used to perform rotational transformation on the laser point using the rotational transformation amount of the point cloud data block, and to perform translational transformation on the laser point after the rotational transformation using the translational transformation amount of the point cloud data block, so as to obtain compensated target point cloud data.

[0052] A third aspect of an embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the point cloud data processing method as described in the first aspect of the embodiment of the present invention are implemented.

[0053] A fourth aspect of an embodiment of the present invention provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the point cloud data processing method as described in the first aspect of the embodiment of the present invention are implemented.

[0054] The point cloud data processing method provided by an embodiment of the present invention obtains the point cloud data blocks in the current acquisition frame of the laser radar and the measurement data of the inertial measurement unit. The point cloud data blocks are obtained by pre-dividing the laser points collected by the laser radar, and each point cloud data block is adjusted to the frame head position of the current acquisition frame. The measurement data is used to generate the rotation transformation amount and translation transformation amount of each point cloud data block, and the point cloud data block corresponding to the laser point is determined. The rotation transformation amount and translation transformation amount of the point cloud data block are used to perform the same motion compensation on the laser point to obtain the compensated target point cloud data. The embodiment of the present invention divides a frame of laser radar data into multiple data blocks, calculates the rotation transformation amount and translation transformation amount of the point cloud data block, and the laser points on each point cloud data block directly use the transformation amount of the data block to perform the same rotation transformation and translation transformation, thereby realizing compensation for the laser radar point cloud motion distortion. There is no need to perform linear interpolation on each point of the current frame point cloud data. Instead, the current frame point cloud data is divided into data blocks, and the laser points on each data block use the same rotation and translation transformation, thereby greatly reducing the amount of interpolation calculation. By transforming the point cloud data to the first moment of the current frame, the changes in posture and three-axis displacement caused by the movement of the vehicle are removed, thereby restoring the true position of the laser point cloud, improving the motion distortion compensation efficiency of the laser radar point cloud data, and realizing accurate reconstruction of the point cloud three-dimensional environment, thereby further improving the safety of autonomous driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0056] Figure 1 is a flowchart of a point cloud data processing method provided by an embodiment of the present invention;

[0057] Figure 2 yes Figure 1 A flowchart of step 101 of a point cloud data processing method provided by an embodiment of the present invention;

[0058] Figure 3 yes Figure 1 A flowchart of step 102 of a point cloud data processing method provided in an embodiment of the present invention;

[0059] Figure 4 yes Figure 1 A flowchart of step 103 of a point cloud data processing method provided by an embodiment of the present invention;

[0060] Figure 5The process of the point cloud data processing method provided by the embodiment of the present invention is Figure 1 ;

[0061] Figure 6 The process of the point cloud data processing method provided by the embodiment of the present invention is Figure 2 ;

[0062] Figure 7 is a scene schematic diagram of a point cloud data processing method provided by an embodiment of the present invention;

[0063] Figure 8 It is a data schematic diagram of a point cloud data processing method provided by an embodiment of the present invention;

[0064] Fig. 9 is a structural block diagram of a point cloud data processing device provided by an embodiment of the present invention;

[0065] Fig.10 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0067] Reference Figure 1 , showing a flow chart of the steps of a point cloud data processing method provided by an embodiment of the present invention, the method may include:

[0068] Step 101, obtaining a point cloud data block in a current acquisition frame of the laser radar and measurement data of an inertial measurement unit; wherein the point cloud data block is obtained by pre-dividing the laser points collected by the laser radar.

[0069] In an embodiment of the present invention, in order to solve the problem that the amount of laser radar point cloud data to be processed in motion distortion compensation is large, the point-by-point linear interpolation calculation is large and inefficient, which affects the motion distortion compensation effect of the laser radar point cloud data, this embodiment divides the laser radar data into data blocks, and the point cloud of each point cloud data block uses the same rotation transformation and translation transformation to achieve laser radar point cloud motion distortion compensation, abandons the quaternion and three-axis position interpolation of each point of the current frame point cloud data, and instead uses the number of UDP message packets received in one frame of data to divide the current frame point cloud data into data blocks, and the point cloud in each point cloud data block uses the same rotation and translation transformation, thereby greatly reducing the amount of interpolation calculation and ensuring the accuracy of compensation.

[0070] It should be noted that, refer to Figure 7 , showing a scene schematic diagram of the point cloud data processing method provided by an embodiment of the present invention. The embodiment of the present invention can use a laser radar set just above the front side of the autonomous driving car to complete the scanning of the horizontal and vertical viewing angles within one cycle to obtain a frame of laser radar point cloud data. The point cloud data of this frame of laser radar contains all the point clouds of the scanned image. The inertial measurement unit (IMU) is a sensor device used to measure and report the speed, direction and gravity influence of an object, and is used to determine the position information of the vehicle.

[0071] In an embodiment of the present invention, in order to provide reliable data support for the distortion compensation of the subsequent laser radar point cloud, after time synchronization of the laser radar and the inertial measurement unit IMU, the processor obtains the point cloud data block in the current acquisition frame of the laser radar and the measurement data of the inertial measurement unit, wherein the point cloud data block is obtained by pre-dividing the laser points collected by the laser radar, and the laser radar data includes the three-axis coordinate information, intensity information, the sequence number of the laser point in the current frame point cloud UDP packet, and the ordered timestamp of each point cloud in the current acquisition frame. Usually, one frame of data contains hundreds of UDP packets. For example, the AT128 laser radar collects 625 UDP packets for one frame of data. The laser points collected in the current acquisition frame of the laser radar are divided according to the number of UDP packets to obtain 625 point cloud data blocks; the measurement data of the inertial measurement unit includes the vehicle's rotation quaternion, three-axis coordinates and timestamp information, which will not be repeated here.

[0072] Step 102, each point cloud data block is adjusted to the frame head position of the current acquisition frame, and the rotation transformation amount and translation transformation amount of each point cloud data block are generated using the measurement data.

[0073] In an embodiment of the present invention, since the laser radar data is first divided into data blocks, the point cloud of each data block uses the same rotation transformation and translation transformation to perform motion distortion compensation. It is necessary to first adjust each point cloud data block to the frame head position of the current acquisition frame, so as to use the measurement data to generate the rotation transformation amount and translation transformation amount of each point cloud data block, wherein the measurement data includes the rotation quaternion change amount and translation change amount between two moments, the ordered timestamp moment of the point cloud data block is between the two moments, and the ordered timestamp of the point cloud data block includes the timestamp of each laser point. According to the rotation quaternion change amount and translation change amount between the two moments, the interpolation parameters from the previous moment to the ordered timestamp moment can be calculated, so as to use the rotation quaternion change amount, translation change amount and interpolation parameters to generate the rotation transformation amount and translation transformation amount of the point cloud data block, the rotation transformation amount is the rotation quaternion of the ordered timestamp of the point cloud data block relative to the IMU measurement data, and the translation transformation amount is the three-axis coordinate of the ordered timestamp of the point cloud data block relative to the IMU measurement data.

[0074] Specifically, the rotational transformation amount and the translational transformation amount of each point cloud data block are calculated. Since the number of point cloud data blocks n is large and the time interval between the beginning and the end of each block is in the microsecond level, the point cloud of each point cloud data block can use the same rotational transformation amount and translational transformation amount to complete the motion distortion compensation. In this embodiment, the rotational transformation amount and the translational transformation amount of the point cloud data block are calculated by finding the ordered timestamps t of the point cloud data block. i , and the ordered timestamps are located at t of the IMU data s and t j First calculate t j Time relative to t s The rotation quaternion change and translation change of t are calculated. i The interpolation rate of change at the moment is calculated based on the rotation quaternion change and the translation change. i Compared to time t s The interpolation parameters are calculated at the moment, so that the ordered timestamps t of the point cloud data blocks are interpolated according to the rotation quaternion change and translation change and the interpolation parameters. i Time compared to t s The rotation transformation q i and the translation transformation v i ,q i The corresponding rotation quaternions w, x, y, and z are v i The corresponding three-axis coordinates x, y, and z are

[0075] Step 103, determining the point cloud data block corresponding to the laser point, using the rotation transformation amount and the translation transformation amount of the point cloud data block to perform the same motion compensation on the laser point, and obtaining compensated target point cloud data.

[0076] In an embodiment of the present invention, the point cloud data block is obtained by pre-dividing the laser points collected by the laser radar. After determining the rotation transformation amount and the translation transformation amount of the point cloud data block, the laser points on the point cloud data block are subjected to the same motion compensation according to the rotation transformation amount and the translation transformation amount of the point cloud data block to obtain compensated target point cloud data. Specifically, using the rotation transformation amount and the translation transformation amount of the point cloud data block to perform the same motion compensation on the laser points includes using the rotation transformation amount of the point cloud data block to perform a rotation transformation on the laser points, and using the translation transformation amount of the point cloud data block to perform a translation transformation on the laser points after the rotation transformation, thereby achieving compensation for the laser radar point cloud data to obtain a complete frame of dedistorted laser point cloud data.

[0077] The point cloud data processing method provided by an embodiment of the present invention obtains the point cloud data blocks in the current acquisition frame of the laser radar and the measurement data of the inertial measurement unit. The point cloud data blocks are obtained by pre-dividing the laser points collected by the laser radar, and each point cloud data block is adjusted to the frame head position of the current acquisition frame. The measurement data is used to generate the rotation transformation amount and translation transformation amount of each point cloud data block, and the point cloud data block corresponding to the laser point is determined. The rotation transformation amount and translation transformation amount of the point cloud data block are used to perform the same motion compensation on the laser point to obtain the compensated target point cloud data. The embodiment of the present invention divides a frame of laser radar data into multiple data blocks, calculates the rotation transformation amount and translation transformation amount of the point cloud data block, and the laser points on each point cloud data block directly use the transformation amount of the data block to perform the same rotation transformation and translation transformation, thereby realizing compensation for the laser radar point cloud motion distortion. There is no need to perform linear interpolation on each point of the current frame point cloud data. Instead, the current frame point cloud data is divided into data blocks, and the laser points on each data block use the same rotation and translation transformation, thereby greatly reducing the amount of interpolation calculation. By transforming the point cloud data to the first moment of the current frame, the changes in posture and three-axis displacement caused by the movement of the vehicle are removed, thereby restoring the true position of the laser point cloud, improving the motion distortion compensation efficiency of the laser radar point cloud data, and realizing accurate reconstruction of the point cloud three-dimensional environment, thereby further improving the safety of autonomous driving.

[0078] Further, see Figure 2 , Figure 2 yes Figure 1 The method flow chart of step 101 of the point cloud data processing method provided in the embodiment of the present invention is substantially the same as the server testing method provided in the first embodiment of the present invention. Step 101 may specifically include the following steps:

[0079] Step 201, pre-time synchronization of the laser radar and the inertial measurement unit.

[0080] It should be noted that, in this embodiment, in order to improve the accuracy of point cloud data processing, it is necessary to ensure the time synchronization of the acquired laser radar and inertial measurement unit data. Therefore, the laser radar and inertial measurement unit are time synchronized in advance. Specifically, time synchronization can be achieved through the following methods: hardware synchronization, using hardware synchronization signals to synchronize the clocks of the laser radar and IMU; software synchronization, by recording the initial timestamp of the laser radar's IMU and calculating the time offset for synchronization. This embodiment does not make specific limitations on this.

[0081] Step 202, obtain the laser points collected by the laser radar in the current acquisition frame, divide the laser points into data blocks using a preset number of message packets, and obtain point cloud data blocks of the current acquisition frame; wherein the number of point cloud data blocks is the same as the preset number of message packets.

[0082] In an embodiment of the present invention, a processor obtains laser points collected by a laser radar in a current acquisition frame, divides the laser points into data blocks using a preset number of message packets, and obtains point cloud data blocks of the current acquisition frame. It should be noted that when using an inertial measurement unit and point cloud data for data processing, the received data is divided and processed. In this embodiment, the UDP protocol is used to receive data, and each frame of data contains multiple UDP message packets. The point cloud data of the current frame is divided into data blocks according to the number of received UDP message packets, and the number of UDP message packets received in the current frame is counted. According to the number of received message packets, the point cloud data is divided into multiple point cloud data blocks, and the number of point cloud data blocks is the same as the preset number of message packets.

[0083] Step 203, obtaining the point cloud data block of the laser radar after time synchronization and the measurement data of the inertial measurement unit.

[0084] The embodiment of the present invention divides the laser points of the current acquisition frame into data blocks, which are divided into multiple point cloud data blocks, so as to facilitate motion compensation of the point clouds on the same point cloud data block with the same posture. There is no need to perform linear interpolation on each point of the current frame point cloud data. Instead, the current frame point cloud data is divided into data blocks, and the laser points on each data block use the same rotation and translation transformation, thereby greatly reducing the amount of interpolation calculation and improving the efficiency of motion distortion compensation of lidar point cloud data.

[0085] Further, see Figure 3 , Figure 3 yes Figure 1 The method flow chart of step 102 of the point cloud data processing method provided in the embodiment of the present invention is substantially the same as the server testing method provided in the first embodiment of the present invention. Step 102 may specifically include the following steps:

[0086] Step 301, determining the target measurement data at the first moment of the current acquisition frame, and using the target measurement data to adjust the point cloud data block to the first moment of the frame.

[0087] In this embodiment, the target measurement data at the beginning of the current acquisition frame is determined, and the point cloud data block is adjusted to the beginning of the frame using the target measurement data. Specifically, the target measurement data at the beginning of the frame is determined using the measurement data before the beginning of the current acquisition frame and the measurement data after the end of the current acquisition frame. The target measurement data includes a rotation quaternion and three-axis coordinates. The rotation quaternion and three-axis coordinates at the beginning of the frame are used to adjust the point cloud data block to the beginning of the frame.

[0088] Specifically, find the IMU data within the ordered timestamp range of the point cloud data block of the frame, and use the IMU before the first moment of the frame as the pose q0 and position v0 at time t0 and the IMU after the end of the frame as t e The pose q e and position v e Interpolation calculates the first frame time t s The pose q s and position v s , using the frame first time t s The pose q s and position v s Compensate all point cloud data blocks to the beginning of the frame t s time.

[0089] Step 302, obtain the ordered timestamp of the point cloud data block, use the ordered timestamp to determine the measurement data at the first moment and the second moment, and obtain the rotation quaternion change and translation change from the first moment to the second moment; wherein the ordered timestamp moment is between the first moment and the second moment.

[0090] In this embodiment, the ordered timestamps of the point cloud data blocks are obtained, and the measured data at the first moment and the second moment are determined by using the ordered timestamps, that is, the ordered timestamps t of the UDP are found. i Located in IMU data t s and t j The ordered timestamp moment is between the first moment and the second moment, and the measurement data of the first moment and the second moment are interpolated to obtain the rotation quaternion change and the translation change from the first moment to the second moment.

[0091] Step 303: Generate a rotational transformation amount and a translational transformation amount of the point cloud data block by using the rotational quaternion variation amount and the translational variation amount from the first moment to the second moment.

[0092] In this embodiment, the rotation quaternion change and the translation change from the first moment to the second moment are used to obtain the interpolation parameters of the ordered timestamps from the first moment to the point cloud data block, and the rotation quaternion change, the translation change and the interpolation parameters are used to generate the rotation transformation amount and the translation transformation amount of the point cloud data block.

[0093] The embodiment of the present invention calculates the rotational transformation amount and the translational transformation amount of the point cloud data block, and the laser point on each point cloud data block directly uses the transformation amount of the data block to perform the same rotational transformation and translational transformation, thereby realizing compensation for the motion distortion of the laser radar point cloud, thereby greatly reducing the amount of interpolation calculation.

[0094] Specifically, step 301 determines the target measurement data at the first moment of the current acquisition frame, and uses the target measurement data to adjust the point cloud data block to the first moment of the frame, which may specifically include:

[0095] Acquire the first measurement data before the beginning moment of the current acquisition frame and the second measurement data after the end moment of the current acquisition frame;

[0096] Using the first measurement data and the second measurement data, determine the target measurement data at the first moment of the frame; wherein the target measurement data includes a rotation quaternion and three-axis coordinates;

[0097] The point cloud data block is adjusted to the first frame moment using the rotation quaternion and the three-axis coordinates.

[0098] It should be noted that in the above steps, the first measurement data before the beginning of the current acquisition frame and the second measurement data after the end of the current acquisition frame are obtained. The first measurement data is the IMU posture q0 and position v0 at time t0 before the beginning of the frame, and the second measurement data is the IMU posture q0 and position v0 at time t0 after the end of the frame. e The pose q e and position v e , use the IMU before the first moment of the frame to correspond to the pose q0 and position v0 at time t0 and the IMU after the end of the frame to correspond to t e The pose q e and position v e Interpolation calculation frame first time t s The pose q s and position v s , compensate the point cloud data block to t s time.

[0099] Specifically, step 303 generates the rotational transformation amount and the translational transformation amount of the point cloud data block at the ordered timestamp moment according to the rotational quaternion variation amount and the translational variation amount, which may specifically include:

[0100] Using the rotation quaternion change and the translation change from the first moment to the second moment, interpolation parameters of the ordered timestamps from the first moment to the point cloud data block are obtained;

[0101] The rotational quaternion variation, the translational variation and the interpolation parameters are used to generate the rotational transformation amount and the translational transformation amount of the point cloud data block.

[0102] It should be noted that in the above steps, refer to Figure 8 , a data schematic diagram of a point cloud data processing method provided by an embodiment of the present invention, using the rotation quaternion change and the translation change from the first moment to the second moment, interpolation calculation to obtain interpolation parameters of the ordered timestamps from the first moment to the point cloud data block, the ordered timestamp time is between the first moment and the second moment, calculate t i The interpolation change rate at the moment, using the interpolation change rate, calculate t i Compared to time t s The interpolation parameters are interpolated at each moment, thereby using the rotation quaternion change, the translation change and the interpolation parameters to generate the rotation transformation amount and the translation transformation amount of the point cloud data block.

[0103] For example, first calculate t j Time relative to t s The rotation quaternion change q trans and translation change v trans , where t s and t j The rotation quaternion and three-axis coordinates are q s ,q j , v s , v j , according to t s and t j The rotation quaternion and spatial position of the rotation translation matrix m are calculated s and m j , calculate t j Time first for t s The rotation and translation matrix change m trans , so according to m trans Calculate q trans and v trans The quaternion interpolation uses the spherical linear interpolation method, and the translation transformation uses the linear transformation method for interpolation. trans and v trans Interpolation calculation t i Compared to time t s Moment interpolation parameters param1 and param2, q trans The corresponding quaternions w, x, y, and z are v transThe corresponding three-axis coordinates x, y, and z are

[0104] In this embodiment, the interpolation parameter can be determined by the following formula:

[0105]

[0106] Among them, param1 is q trans t i Compared to time t s The interpolation parameter at the moment, param2 is v trans t i Compared to time t s The interpolation parameters at time, Q trans The corresponding quaternion w, r i Yes i The interpolated rate of change at time instant.

[0107] In this embodiment, according to q trans and v trans Interpolation calculation t i Compared to time t s The quaternion q i And the three-axis coordinates v i ,q i The corresponding quaternions w, x, y, and z are v i The corresponding three-axis coordinates x, y, and z are

[0108]

[0109] v i =r i *v trans

[0110] Further, see Figure 4 , Figure 4 yes Figure 1 The method flow chart of step 103 of the point cloud data processing method provided in the embodiment of the present invention is substantially the same as the server testing method provided in the first embodiment of the present invention. Step 103 may specifically include the following steps:

[0111] Step 401, determining the serial number of the laser point and the number of laser points in the point cloud data block;

[0112] In this embodiment, the point cloud data blocks are divided according to the preset number of message packets, and the divided laser points are finely arranged in the order of division. If it is necessary to determine which point cloud data block the laser point belongs to, the serial number of the laser point and the number of laser points in the point cloud data block are first determined, and the serial number of the laser point is calculated from the starting point of the point cloud data. Since the point cloud data blocks are divided according to the preset number of message packets, the number of laser points in each point cloud data block is the same.

[0113] Step 402: Determine the point cloud data block to which the laser point belongs based on the serial number of the laser point and the number of laser points in the point cloud data block.

[0114] It should be noted that the point cloud data block to which the laser point belongs can be determined based on the serial number of the laser point and the number of laser points in the point cloud data block. For example, if there is a laser point serial number point_index, the serial number is calculated from the starting point of the point cloud data, and the point cloud data is divided into multiple point cloud data blocks. If each point cloud data block contains block_size laser points, the serial number point_index of the laser point and the size block_size of each point cloud data block can be used to calculate the point cloud data block number to which the laser point belongs. Specifically, the serial number point_index of the laser point is divided by the size block_size of each point cloud data block, and the integer part of the returned quotient can be used to calculate the data block number block_index to which the laser point belongs, that is, to determine the point cloud data block to which the laser point belongs.

[0115] Step 403: The laser point is subjected to rotational transformation using the rotational transformation amount of the point cloud data block, and the laser point subjected to rotational transformation is subjected to translational transformation using the translational transformation amount of the point cloud data block, so as to obtain compensated target point cloud data.

[0116] In this embodiment, the laser points on the point cloud data block are subjected to the same motion distortion compensation according to the rotation transformation amount and translation transformation amount of the point cloud data block, that is, the laser points are subjected to rotation transformation using the rotation transformation amount and the translation transformation amount of the point cloud data block, and after the rotation transformation is completed, the laser points subjected to rotation transformation are subjected to translation transformation using the translation transformation amount of the point cloud data block, thereby obtaining compensated target point cloud data. Specifically, each point cloud data block of the current frame includes a preset number of laser points, and since the point cloud data blocks are divided in sequence, the UDP packet, i.e., the point cloud data block, to which the laser point belongs can be determined according to the sequence number of the laser point and the number of laser points in each point cloud data block, thereby performing motion distortion compensation on the laser radar point cloud data, first performing rotation transformation and then performing translation transformation. For example, the original three-axis coordinate of point p corresponding to point p is v p , point p is in the corresponding nth UDP packet, and the quaternion q corresponding to the nth packet is n And the three-axis coordinates vn , the three-axis coordinates after motion distortion compensation are v′ p , v′ p =q n *v p +v n , calculate the point cloud data after motion distortion compensation of the current frame of lidar point cloud data.

[0117] The embodiment of the present invention divides the point cloud data of the current frame into data blocks, and the laser points on each data block use the same rotation and translation transformation, thereby greatly reducing the amount of interpolation calculations, improving the efficiency of motion distortion compensation of the lidar point cloud data, and achieving accurate reconstruction of the point cloud three-dimensional environment.

[0118] In order to enable those skilled in the art to more clearly understand the overall process of the point cloud data processing method disclosed in the above embodiments of the present invention, refer to Figure 5 , the process of the point cloud data processing method provided by the embodiment of the present invention Figure 1 , as an example to illustrate.

[0119] S501, laser radar and IMU time and space synchronization;

[0120] S502, receiving laser radar data;

[0121] S503, preprocessing the data to obtain a point cloud data block;

[0122] S504, obtaining IMU data;

[0123] S505, obtaining the vehicle position;

[0124] S506, performing motion distortion compensation on the point cloud data of the laser radar;

[0125] S507, output the compensated laser radar point cloud data.

[0126] It should be noted that in the above steps, the laser radar and IMU are synchronized in time and space to ensure that the data of the laser radar and IMU are aligned in time and space. The laser radar data is received through the UDP protocol, and the data is preprocessed to obtain a point cloud data block. Specifically, the laser radar data is preprocessed according to the number of UDP protocol packets and divided into multiple point cloud data blocks. While obtaining the laser radar data, the IMU data is received and parsed, and the posture of the vehicle is calculated based on the IMU data, where the posture includes a rotation quaternion and three-axis coordinates. The point cloud data of the laser radar is motion-distorted according to the posture of the vehicle, and the laser radar point cloud data after motion distortion compensation is output.

[0127] In order to enable those skilled in the art to more clearly understand the overall process of the point cloud data processing method disclosed in the above embodiments of the present invention, refer to Figure 6 , the process of the point cloud data processing method provided by the embodiment of the present invention Figure 2 , as an example to illustrate.

[0128] S601, the laser radar point cloud is divided into data blocks according to the UDP packet;

[0129] S602, calculating the rotation quaternion and three-axis coordinates of the laser radar point cloud frame at the first moment;

[0130] S603, calculating qtrans and vtans at time tj relative to time ts;

[0131] S604, calculating the interpolation change rate ri;

[0132] S605, calculating qi and vi at time ti relative to time ts;

[0133] S606, performing rotation transformation and translation transformation according to qi and vi.

[0134] It should be noted that in the above steps, the lidar point cloud is divided into data blocks according to the UDP packets to obtain point cloud data blocks with the same number of UDP data packets. The measurement data is used to calculate the rotation quaternion and three-axis coordinates of the lidar point cloud frame at the first moment, and the IMU before the first moment of the frame corresponds to the pose q0 and position v0 at the time t0 and the IMU after the end of the frame corresponds to the pose q0 and position v0 at the time t0. e The pose q e and position v e Interpolation calculation frame first time t s The pose q s and position v s , compensate all point cloud data blocks to t s At this moment, after compensation, find the ordered timestamp t of UDP i Located in IMU data t s and t j Between, calculate t j Time relative to t s The rotation quaternion change q trans and translation change v trann , calculate t i The interpolation rate of change at time r i ,in, According to q trans and v trans Interpolation calculation t i Compared to time t s The quaternion q i And the three-axis coordinates v i ,qi The corresponding quaternions w, x, y, and z are v i The corresponding three-axis coordinates x, y, and z are

[0135] Specifically, the rotation and translation transformations corresponding to all UDP packets of the current frame point cloud are performed. According to the UDP packet to which each point belongs, the motion distortion compensation is performed on the lidar point cloud data. The rotation transformation is performed first, and then the translation transformation is performed. For example, the original three-axis coordinates of point p are v p , point p is in the corresponding nth UDP packet, and the quaternion q corresponding to the nth packet is n And the three-axis coordinates v n , the three-axis coordinates after motion distortion compensation are v′ p , v′ p =q n *v p +v n , calculate the point cloud data after motion distortion compensation of the current frame of lidar point cloud data.

[0136] Reference Fig. 9 , shows a structural block diagram of a point cloud data processing device provided by an embodiment of the present invention, and the device may include:

[0137] The data acquisition module 701 is used to acquire the point cloud data block in the current acquisition frame of the laser radar and the measurement data of the inertial measurement unit; wherein the point cloud data block is obtained by dividing the laser points collected by the laser radar in advance;

[0138] A compensation amount generating module 702 is used to adjust each point cloud data block to the frame head position of the current acquisition frame, and use the measurement data to generate the rotational transformation amount and the translational transformation amount of each point cloud data block;

[0139] The data compensation module 703 is used to determine the point cloud data block corresponding to the laser point, and use the rotation transformation amount and the translation transformation amount of the point cloud data block to perform the same motion compensation on the laser point to obtain compensated target point cloud data.

[0140] Furthermore, the data acquisition module 701 includes:

[0141] A synchronization submodule, used to pre-synchronize the laser radar and the inertial measurement unit;

[0142] A division submodule is used to obtain the laser points collected by the laser radar in the current acquisition frame, divide the laser points into data blocks using a preset number of message packets, and obtain point cloud data blocks of the current acquisition frame; wherein the number of the point cloud data blocks is the same as the preset number of message packets;

[0143] The acquisition submodule is used to obtain the point cloud data blocks of the laser radar after time synchronization and the measurement data of the inertial measurement unit.

[0144] Furthermore, the compensation amount generating module 702 includes:

[0145] An adjustment submodule, used to determine the target measurement data at the first moment of the current acquisition frame, and use the target measurement data to adjust the point cloud data block to the first moment of the frame;

[0146] A first determination submodule is used to obtain an ordered timestamp of a point cloud data block, and use the ordered timestamp to determine the measurement data of a first moment and a second moment, so as to obtain a rotation quaternion change and a translation change from the first moment to the second moment; wherein the ordered timestamp is between the first moment and the second moment;

[0147] The generating submodule is used to generate the rotational transformation amount and the translational transformation amount of the point cloud data block by using the rotational quaternion change amount and the translational change amount from the first moment to the second moment.

[0148] Furthermore, the adjustment submodule includes:

[0149] An acquisition unit, used to acquire first measurement data before the beginning moment of a current acquisition frame and second measurement data after the end moment of the current acquisition frame;

[0150] A determination unit, configured to determine target measurement data at the frame start time by using the first measurement data and the second measurement data; wherein the target measurement data includes a rotation quaternion and three-axis coordinates;

[0151] The adjustment unit is used to adjust the point cloud data block to the first frame moment by using the rotation quaternion and the three-axis coordinates.

[0152] Furthermore, the generation submodule includes:

[0153] A calculation unit, used to obtain interpolation parameters of ordered timestamps from the first moment to the point cloud data block by using the rotation quaternion change and the translation change from the first moment to the second moment;

[0154] The generating unit is used to generate the rotational transformation amount and the translational transformation amount of the point cloud data block by using the rotational quaternion variation amount, the translational variation amount and the interpolation parameter.

[0155] Furthermore, the data compensation module 703 includes:

[0156] The second determination submodule is used to determine the serial number of the laser point and the number of laser points in the point cloud data block;

[0157] A third determination submodule is used to determine the point cloud data block to which the laser point belongs according to the sequence number of the laser point and the number of laser points in the point cloud data block;

[0158] The transformation submodule is used to perform rotational transformation on the laser point using the rotational transformation amount of the point cloud data block, and to perform translational transformation on the laser point after the rotational transformation using the translational transformation amount of the point cloud data block, so as to obtain compensated target point cloud data.

[0159] The point cloud data processing device provided by an embodiment of the present invention obtains the point cloud data blocks in the current acquisition frame of the laser radar and the measurement data of the inertial measurement unit. The point cloud data blocks are obtained by pre-dividing the laser points collected by the laser radar, and each point cloud data block is adjusted to the frame head position of the current acquisition frame. The measurement data is used to generate the rotation transformation amount and translation transformation amount of each point cloud data block, and the point cloud data block corresponding to the laser point is determined. The rotation transformation amount and translation transformation amount of the point cloud data block are used to perform the same motion compensation on the laser point to obtain compensated target point cloud data. The embodiment of the present invention divides a frame of laser radar data into multiple data blocks, calculates the rotation transformation amount and translation transformation amount of the point cloud data block, and the laser points on each point cloud data block directly use the transformation amount of the data block to perform the same rotation transformation and translation transformation, thereby realizing compensation for the laser radar point cloud motion distortion. There is no need to perform linear interpolation on each point of the current frame point cloud data. Instead, the current frame point cloud data is divided into data blocks, and the laser points on each data block use the same rotation and translation transformation, thereby greatly reducing the amount of interpolation calculation. By transforming the point cloud data to the first moment of the current frame, the changes in posture and three-axis displacement caused by the movement of the vehicle are removed, thereby restoring the true position of the laser point cloud, improving the motion distortion compensation efficiency of the laser radar point cloud data, and realizing accurate reconstruction of the point cloud three-dimensional environment, thereby further improving the safety of autonomous driving.

[0160] Reference Fig.10 , an embodiment of the present invention further provides an electronic device, such as Fig.10 As shown, it includes a processor 801, a communication interface 802, a memory 803 and a communication bus 804, wherein the processor 801, the communication interface 802, and the memory 803 communicate with each other through the communication bus 804.

[0161] Memory 803, used for storing computer programs;

[0162] The processor 801 is used to execute the program stored in the memory 803, and implements the following steps:

[0163] Obtaining a point cloud data block in a current acquisition frame of the laser radar and measurement data of an inertial measurement unit; wherein the point cloud data block is obtained by pre-dividing the laser points collected by the laser radar;

[0164] Adjusting each point cloud data block to the frame head position of the current acquisition frame, and using the measurement data to generate the rotational transformation amount and the translational transformation amount of each point cloud data block;

[0165] The point cloud data block corresponding to the laser point is determined, and the rotation transformation amount and the translation transformation amount of the point cloud data block are used to perform the same motion compensation on the laser point to obtain compensated target point cloud data.

[0166] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0167] The communication interface is used for communication between the above terminal and other devices.

[0168] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0169] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0170] In another embodiment provided by the present invention, a computer-readable storage medium is also provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the point cloud data processing method described in any one of the above embodiments.

[0171] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0172] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0173] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0174] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A point cloud data processing method, characterized in that: The method comprises: Obtaining a point cloud data block in a current acquisition frame of the laser radar and measurement data of an inertial measurement unit; wherein the point cloud data block is obtained by pre-dividing the laser points collected by the laser radar; Adjusting each point cloud data block to the frame head position of the current acquisition frame, and using the measurement data to generate the rotational transformation amount and the translational transformation amount of each point cloud data block; The point cloud data block corresponding to the laser point is determined, and the rotation transformation amount and the translation transformation amount of the point cloud data block are used to perform the same motion compensation on the laser point to obtain compensated target point cloud data.

2. The method according to claim 1, characterized in that The point cloud data block of the current acquisition frame of the laser radar and the measurement data of the inertial measurement unit are obtained; wherein the point cloud data block is obtained by pre-dividing the laser points collected by the laser radar, including: Pre-synchronize the time of the LiDAR and the IMU; Obtaining laser points collected by the laser radar in the current acquisition frame, dividing the laser points into data blocks using a preset number of message packets, and obtaining point cloud data blocks of the current acquisition frame; wherein the number of the point cloud data blocks is the same as the preset number of message packets; Get the time-synchronized point cloud data blocks of the LiDAR and the measurement data of the inertial measurement unit.

3. The method according to claim 1, characterized in that The step of adjusting each point cloud data block to the frame head position of the current acquisition frame and using the measurement data to generate the rotation transformation amount and the translation transformation amount of each point cloud data block includes: Determine the target measurement data at the first moment of the current acquisition frame, and use the target measurement data to adjust the point cloud data block to the first moment of the frame; Obtaining ordered timestamps of the point cloud data block, using the ordered timestamps to determine the measurement data at the first moment and the second moment, and obtaining the rotation quaternion change and the translation change from the first moment to the second moment; wherein the ordered timestamp is between the first moment and the second moment; The rotational quaternion change and the translational change from the first moment to the second moment are used to generate the rotational transformation amount and the translational transformation amount of the point cloud data block.

4. The method according to claim 3, characterized in that The determining of the target measurement data at the first moment of the current acquisition frame, and adjusting the point cloud data block to the first moment of the frame by using the target measurement data, comprises: Acquire the first measurement data before the beginning moment of the current acquisition frame and the second measurement data after the end moment of the current acquisition frame; Using the first measurement data and the second measurement data, determine the target measurement data at the first moment of the frame; wherein the target measurement data includes a rotation quaternion and three-axis coordinates; The point cloud data block is adjusted to the first frame moment using the rotation quaternion and the three-axis coordinates.

5. The method according to claim 3, characterized in that: The step of using the rotation quaternion variation and the translation variation from the first moment to the second moment to generate the rotation transformation amount and the translation transformation amount of the point cloud data block comprises: Using the rotation quaternion change and the translation change from the first moment to the second moment, interpolation parameters of the ordered timestamps from the first moment to the point cloud data block are obtained; The rotational quaternion variation, the translational variation and the interpolation parameters are used to generate the rotational transformation amount and the translational transformation amount of the point cloud data block.

6. The method according to claim 1, characterized in that The step of determining a point cloud data block corresponding to the laser point and using a rotational transformation amount and a translational transformation amount of the point cloud data block to perform motion compensation on the laser point to obtain compensated target point cloud data includes: Determine the serial number of the laser point and the number of laser points in the point cloud data block; According to the serial number of the laser point and the number of laser points in the point cloud data block, determine the point cloud data block to which the laser point belongs; The laser point is subjected to rotational transformation using the rotational transformation amount of the point cloud data block, and the laser point subjected to rotational transformation is subjected to translational transformation using the translational transformation amount of the point cloud data block, so as to obtain compensated target point cloud data.

7. A point cloud data processing device, characterized in that: The device comprises: A data acquisition module is used to acquire a point cloud data block in a current acquisition frame of the laser radar and measurement data of an inertial measurement unit; wherein the point cloud data block is obtained by pre-dividing the laser points collected by the laser radar; A compensation amount generation module is used to adjust each point cloud data block to the frame head position of the current acquisition frame, and use the measurement data to generate the rotational transformation amount and the translational transformation amount of each point cloud data block; The data compensation module is used to determine the point cloud data block corresponding to the laser point, and use the rotation transformation amount and translation transformation amount of the point cloud data block to perform the same motion compensation on the laser point to obtain compensated target point cloud data.

8. The device according to claim 7, characterized in that The data acquisition module includes: A synchronization submodule, used to pre-synchronize the laser radar and the inertial measurement unit; A division submodule is used to obtain the laser points collected by the laser radar in the current acquisition frame, divide the laser points into data blocks using a preset number of message packets, and obtain point cloud data blocks of the current acquisition frame; wherein the number of the point cloud data blocks is the same as the preset number of message packets; The acquisition submodule is used to obtain the point cloud data blocks of the laser radar after time synchronization and the measurement data of the inertial measurement unit.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by the processor, the point cloud data processing method according to any one of claims 1 to 6 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the point cloud data processing method according to any one of claims 1 to 6 is implemented.

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