Compression transmission method and apparatus for point cloud data of laser radar, and electronic device

Abstract

Embodiments of the present disclosure relates to the technical field of autonomous driving, and provide a compression transmission method and apparatus for point cloud data of a laser radar, and an electronic device, for solving the problem of high resource consumption in storing and transmitting point cloud data of a laser radar. The compression transmission method for point cloud data of a laser radar provided by the embodiments of the present disclosure comprises: respectively acquiring a first frame of point cloud data and a second frame of point cloud data of a laser radar, wherein the first frame of point cloud data comprises depth value information, horizontal angle information and vertical angle information of a point, and the second frame of point cloud data comprises depth value information of a point; and respectively associating the first frame of point cloud data and the second frame of point cloud data to a first image and a second image to obtain compressed image bitstreams of the first frame of point cloud data and the second frame of point cloud data. According to the compression transmission method for point cloud data of a laser radar provided by the embodiments of the present disclosure, on the basis of the scanning characteristic of a rotating laser radar that the emission angle remains unchanged in a stable scanning process, the storage and transmission of redundant information are reduced, and the data volume is further compressed.

Description

Compression transmission method, device and electronic equipment for laser radar point cloud data

[0001] This disclosure claims priority to Chinese patent application number 202311031445.1, filed with the State Intellectual Property Office of China on August 16, 2023, entitled “Compression and transmission method, device, electronic device and storage medium for laser point cloud data”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] The embodiments of the present disclosure relate to the field of autonomous driving technology, and more particularly to a method, device, electronic device, and storage medium for compressing and transmitting lidar point cloud data. Background Art

[0003] LiDAR (LiDAR) is a commonly used sensor in autonomous driving technology due to its stability and minimal sensitivity to lighting. It provides three-dimensional geometric information about the environment surrounding an autonomous vehicle. With advancements in LiDAR manufacturing technology and improvements in LiDAR wiring harnesses, the number of 3D points captured by LiDAR scans is also increasing, leading to a continuous increase in the resources required to store and transmit LiDAR point cloud data. Therefore, compressing LiDAR point cloud data to reduce the resources required for storage and transmission has become a pressing issue. Summary of the Invention

[0004] The embodiments of the present disclosure provide a method, apparatus, device, and storage medium for compressing lidar point cloud data, which effectively reduce the storage and transmission volume of point cloud data while ensuring accurate recovery of the point cloud data.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for compressing and transmitting lidar point cloud data, comprising:

[0006] Acquire a first frame of point cloud data from a laser radar, where the first frame of point cloud data includes depth value information, horizontal angle information, and vertical angle information of the point;

[0007] Associating depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data with the first image to obtain a compressed image bit stream of the first frame of point cloud data;

[0008] Acquire a second frame of point cloud data from the laser radar, where the second frame of point cloud data includes depth value information of the points;

[0009] Associating the second frame of point cloud data including depth value information of the points with the second image to obtain a compressed image bit stream of the second frame of point cloud data;

[0010] The horizontal angle information and the vertical angle information of the points included in the second frame of point cloud data can be calculated based on the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data.

[0011] Furthermore, the time lengths of the first frame of point cloud data and the second frame of point cloud data are the same.

[0012] Furthermore, there is a preset time interval between the first frame of point cloud data and the second frame of point cloud data.

[0013] Furthermore, the number of rows of the first image and the second image respectively corresponds to the number of lines of the laser radar, and the number of columns of the first image and the second image respectively corresponds to the number of single-circle scans of the laser radar.

[0014] Optionally, the number of lines of the laser radar is 32 lines, 64 lines or 128 lines, and the number of single-circle scans of the laser radar is greater than 500.

[0015] Optionally, the first image includes at least one multi-channel image, and the number of channels of the multi-channel image corresponds to the type of data included in the first frame of point cloud data.

[0016] Optionally, the first image includes multiple single-channel images, and the number of the single-channel images corresponds to the type of data included in the first frame of point cloud data.

[0017] Optionally, the second image includes at least one multi-channel image, and the number of channels of the multi-channel image corresponds to the type of data included in the second frame of point cloud data.

[0018] Optionally, the second image includes multiple single-channel images, and the number of the single-channel images corresponds to the type of data included in the second frame of point cloud data.

[0019] Furthermore, the data types include at least the depth value information of the point, the horizontal emission angle information, the vertical emission angle information, and the intensity value information of the laser radar.

[0020] In a second aspect, an embodiment of the present disclosure provides a method for decompressing lidar point cloud data, comprising:

[0021] Receive a first compressed image bit stream, and obtain depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data, as well as corresponding intensity value information of the laser radar;

[0022] Storing horizontal angle information and vertical angle information of points included in the first frame of point cloud data;

[0023] Receive a second compressed image bit stream, and obtain depth value information of points included in the second frame of point cloud data, and corresponding intensity value information of the laser radar;

[0024] The horizontal angle information and the vertical angle information of the points in the second frame of point cloud data can be calculated based on the horizontal angle information and the vertical angle information of the points in the first frame of point cloud data.

[0025] In a third aspect, an embodiment of the present disclosure provides a point cloud construction method, including:

[0026] Receive a first compressed image bit stream, and obtain depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data, as well as corresponding laser radar intensity value information;

[0027] Storing horizontal angle information and vertical angle information of points included in the first frame of point cloud data;

[0028] receiving a second compressed image bitstream, and obtaining depth value information of points included in a second frame of point cloud data, and corresponding intensity value information of the laser radar; wherein the horizontal angle information and vertical angle information of the points included in the second frame of point cloud data can be calculated based on the horizontal angle information and vertical angle information of the points in the first frame of point cloud data;

[0029] Recalling the stored horizontal angle information and vertical angle information of the points included in the first frame of point cloud data, and obtaining the horizontal angle information and vertical angle information of the corresponding points in the second frame of point cloud data;

[0030] The final point cloud is constructed based on the depth value information, horizontal angle information and vertical angle information of the points included in the first frame point cloud data, and the corresponding intensity value information of the laser radar, as well as the depth value information of the points included in the second frame point cloud data, the corresponding intensity value information of the laser radar, and the horizontal angle information and vertical angle information of the corresponding points obtained in the second frame point cloud data.

[0031] In a fourth aspect, an embodiment of the present disclosure provides a laser radar point cloud data compression and transmission device, comprising:

[0032] An acquisition module is configured to acquire a first frame of point cloud data and a second frame of point cloud data from a laser radar, wherein the first frame of point cloud data includes depth value information, horizontal angle information, and vertical angle information of the point, and the second frame of point cloud data includes depth value information of the point;

[0033] a processing module, configured to associate the first frame of point cloud data, including the depth value information, horizontal angle information, and vertical angle information of the points, with a first image to obtain a compressed image bitstream of the first frame of point cloud data; and to associate the second frame of point cloud data, including the depth value information of the points, with a second image to obtain a compressed image bitstream of the second frame of point cloud data;

[0034] a transmission module, configured to transmit the compressed image bit stream of the first frame of point cloud data and the compressed image bit stream of the second frame of point cloud data,

[0035] The horizontal angle information and the vertical angle information of the points included in the second frame of point cloud data can be calculated based on the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data.

[0036] In a fifth aspect, an embodiment of the present disclosure provides a laser radar point cloud data decompression device, comprising:

[0037] A receiving module, configured to receive a first compressed image bit stream and a second compressed image bit stream;

[0038] a processing module, configured to obtain, from the first compressed image bitstream, depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data, as well as corresponding intensity value information of the laser radar; and to obtain, from the second compressed image bitstream, depth value information of points included in the second frame of point cloud data, as well as corresponding intensity value information of the laser radar;

[0039] a storage module, configured to store horizontal angle information and vertical angle information of points included in the first frame of point cloud data;

[0040] a calling module, configured to call the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data from the storage module, and obtain the horizontal angle information and the vertical angle information of the corresponding points in the second frame of point cloud data;

[0041] The horizontal angle information and the vertical angle information of the points included in the second frame of point cloud data can be calculated based on the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data.

[0042] In a sixth aspect, an embodiment of the present disclosure provides a point cloud construction device, comprising:

[0043] A receiving module, configured to receive a first compressed image bit stream and a second compressed image bit stream;

[0044] a processing module, configured to obtain, from the first compressed image bitstream, depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data, as well as corresponding laser radar intensity value information; and to obtain, from the second compressed image bitstream, depth value information of points included in the second frame of point cloud data, as well as corresponding laser radar intensity value information;

[0045] a storage module, configured to store horizontal angle information and vertical angle information of points included in the first frame of point cloud data;

[0046] a calling module, configured to call the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data from the storage module, and obtain the horizontal angle information and the vertical angle information of the corresponding points in the second frame of point cloud data;

[0047] A construction module is used to construct a final point cloud based on the depth value information, horizontal angle information and vertical angle information of the points included in the first frame of point cloud data, and the corresponding intensity value information of the laser radar, as well as the depth value information of the points included in the second frame of point cloud data, the corresponding intensity value information of the laser radar, and the horizontal angle information and vertical angle information of the corresponding points obtained in the second frame of point cloud data.

[0048] In a seventh aspect, an embodiment of the present disclosure provides an electronic device, including:

[0049] one or more processors;

[0050] A storage unit for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the aforementioned laser radar point cloud data compression transmission method, and / or the aforementioned laser radar point cloud data decompression method, and / or the aforementioned point cloud construction method.

[0051] In an eighth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can implement the aforementioned method for compressing and transmitting lidar point cloud data, and / or the aforementioned method for decompressing lidar point cloud data, and / or the aforementioned method for constructing a point cloud.

[0052] The laser radar point cloud data compression method and device provided in the embodiments of the present disclosure utilize the scanning characteristic of the rotating laser radar in which the emission angle remains unchanged during a stable scanning process. After acquiring the first frame of point cloud data, the angle data in subsequent frames are omitted, thereby reducing the storage and transmission of redundant information and further compressing the data volume of the point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] FIG1 is a schematic diagram of a flow chart of a method for compressing laser radar point cloud data according to an embodiment of the present disclosure;

[0054] FIG2 is a schematic diagram of a laser radar scan according to an embodiment of the present disclosure;

[0055] FIG3 is a schematic diagram of associating lidar point cloud data with an image in one embodiment of the present disclosure;

[0056] FIG4 is a schematic diagram of storing lidar point cloud data in a single multi-channel image according to an embodiment of the present disclosure;

[0057] FIG5 is a schematic diagram of storing lidar point cloud data in multiple single-channel images according to an embodiment of the present disclosure;

[0058] FIG6 is a schematic diagram of obtaining different frames of laser radar data according to an embodiment of the present disclosure;

[0059] FIG7 is a schematic structural diagram of a laser radar point cloud data compression device according to an embodiment of the present disclosure;

[0060] FIG8 is a flowchart of an example of laser radar point cloud data compression according to an embodiment of the present disclosure;

[0061] FIG9 is a flow chart of a method for decompressing laser radar point cloud data according to an embodiment of the present disclosure;

[0062] FIG10 is a schematic structural diagram of a laser radar point cloud data decompression device according to an embodiment of the present disclosure;

[0063] FIG11 is a schematic diagram of a process of constructing a point cloud according to an embodiment of the present disclosure;

[0064] FIG12 is a schematic structural diagram of a point cloud construction device according to an embodiment of the present disclosure;

[0065] FIG13 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. Implementation Method

[0066] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. It should be noted that the embodiments described in the specific embodiments of the present disclosure are merely examples of the embodiments of the present disclosure. Based on the embodiments in the specific embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the embodiments of the present disclosure.

[0067] As shown in FIG1 , a flow chart of a method for compressing and transmitting lidar point cloud data provided by an embodiment of the present disclosure is shown. The lidar point cloud data compression method 100 includes:

[0068] Step 101: Acquire a first frame of point cloud data from a laser radar, where the first frame of point cloud data includes depth value information, horizontal angle information, and vertical angle information of the point;

[0069] Step 102: Associating depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data with a first image;

[0070] Specifically, referring to Figure 2, a schematic diagram of a laser radar scanning and emitting laser points in an embodiment of the present disclosure is shown. In this embodiment, a mechanical rotating laser radar is used as an example for illustration. In actual use, other types of laser radars may also be used, and each case is not described in detail here.

[0071] As shown in Figure 2, a mechanical rotating LiDAR emits a laser beam at a fixed angle during scanning, with the beam aligned horizontally and vertically at specific angles. After striking an object, the laser beam reflects back to the LiDAR receiver, which then calculates the LiDAR's depth and intensity based on the time difference.

[0072] Take any three-dimensional point r in space p For example (see Figure 3 for details), the three-dimensional point r obtained by the laser radar p Point cloud data, including the three-dimensional point r p The coordinates in the Cartesian coordinate system (x p ,y p ,z p ), the rotation angle a of the laser radar beam p , and the pitch angle ß of the lidar beam p According to the rotation angle a of the laser radar beam p , and the pitch angle ß of the lidar beam p , the three-dimensional point r p Projected on the first picture shown in Figure 3, the pitch angle of the laser radar beam is ß p Determines the row of the first image where the point is located and the rotation angle a of the laser radar beam p Determine the column of the first image that the point is in.

[0073] Among them, the pitch angle ß of the laser radar beam p Corresponding to the laser radar beam, the rotation angle a of the laser radar p Corresponding to the number of single-circle scans of the laser radar. For example, in the embodiments of the present application, the number of lines of the laser radar used is 32, 64, or 128, and the number of single-circle scans of the laser radar is greater than 500. It should be noted that the above examples are merely illustrative of the embodiments of the present disclosure and are not intended to limit the embodiments of the present disclosure.

[0074] Step 103: compress the first image to obtain a compressed image bit stream of the first frame of point cloud data;

[0075] Exemplarily, the method for compressing the image can be an image compression method commonly used in the field of images. For example, in an example where some distortion of distance or intensity information can be tolerated, JPEG or JPEG2000 can be used to compress the distance image, intensity image or combined color image. In an example where no compression is required, a JPEG-LS or FFV1 encoder is used. It should be noted that the embodiments of the present disclosure do not limit the specific image compression method. On the premise that the solution of the embodiments of the present disclosure can be implemented, those skilled in the art can use any image compression encoder that can perform image compression.

[0076] Step 104: Acquire a second frame of point cloud data from the laser radar. The second frame of point cloud data includes depth value information of the points, but does not include horizontal angle information and vertical angle information.

[0077] Step 105 , associating depth value information of the points included in the second frame of point cloud data with the second image;

[0078] Step 106: compress the second image to obtain a compressed image bit stream of the second frame of point cloud data;

[0079] Step 107: Send and transmit the compressed image bit stream of the first frame of point cloud data and the compressed image bit stream of the second frame of point cloud data.

[0080] It should be noted that the compressed image bit stream of the first frame point cloud data and the compressed image bit stream of the second frame point cloud data can be sent at the same time or transmitted in time-sharing. The specific sending interval and method do not serve as a limitation to the implementation methods of the present disclosure.

[0081] In one example, referring to FIG4 , the first image and the second image may be a multi-channel image. Each channel in the multi-channel image stores one of the depth value information, the horizontal emission angle information, the vertical emission angle information, and attribute information related to the lidar, such as an intensity value.

[0082] In another example, referring to FIG5 , the first image and the second image may be multiple single-channel images. Each of the multiple single-channel images stores one of the depth value information, the horizontal emission angle information, the vertical emission angle information, and related attribute information of the lidar, such as an intensity value.

[0083] Taking Figure 5 as an example, for a rotating mechanical laser radar with m lines and n points on a single line, its scanned three-dimensional points can be projected onto an image with a width of n and a height of m. Since the starting index of the image rows and columns is 0, the row and column indexes of the image projected by the laser radar are 0~m-1 rows and 0~n-1 columns. According to the method in the first embodiment of the present disclosure, the depth value, horizontal angle value and vertical angle value of each point are calculated, and then the calculated depth value, horizontal angle value and vertical angle value, as well as other attribute values ​​of the laser radar, such as intensity, are quantized to 16 bits respectively and saved in the pixels of the corresponding rows and columns of the multi-channel image.

[0084] Furthermore, referring to FIG2 , it can be seen that when the rotating laser radar is scanning stably, the emission angle of each point in each frame of the point cloud is fixed. Therefore, there is no need to repeatedly calculate this angle value. The angle value information of the second frame, the third frame, and up to the N-1 frame can be inferred based on the angle value information of the first frame of the point cloud data, where N is a preset value. For the first frame of point cloud data obtained by the laser radar, it contains the above-mentioned depth value information, horizontal angle value information, and vertical angle value information, as well as other attribute values, such as intensity; for the subsequent 2nd, 3rd, to N-1 frames, their angle value information can be inferred based on the angle information of the first frame of the point cloud data. For the above-mentioned 2nd, 3rd, to N-1 frames of point cloud data, their angle value information is omitted, and only other information that does not contain angle value information is retained. This can reduce the amount of repeated information transmitted and further reduce the amount of data transmitted.

[0085] It should be noted that the second frame of point cloud data has the same time length as the first frame of point cloud data, and there is a preset time interval between the second frame of point cloud data and the first frame of point cloud data, for example, 0-300 milliseconds.

[0086] The laser radar point cloud data compression and transmission method of the disclosed embodiment collects point cloud data obtained by the laser radar so that the first bit stream of the first frame of point cloud data contains angle value information; for the second frame, third frame, and up to the N-1 frame of point cloud data subsequent to the first frame of point cloud data, their angle value information can be inferred based on the angle information of the first frame of point cloud data, so that they do not contain angle value information, thereby further compressing the data volume.

[0087] As shown in FIG6 , it is an example diagram of obtaining different frame data of the laser radar according to an embodiment of the present disclosure.

[0088] First, refer to Figure 6A. In the example diagram shown in Figure 6A, the point cloud data acquired by the laser radar for each rotation is regarded as a frame of point cloud data. In a specific implementation, a certain rotation during the laser radar's stable scanning can be set as the first frame of point cloud data. Similarly, the data acquired in the corresponding second, third, and N-1th rotations are respectively regarded as the second, third, and N-1th frames of point cloud data. When sending point cloud data, it is only necessary to obtain and transmit the angle value information contained in the first frame of point cloud data. The angle value information contained in the second, third, and N-1th frames of point cloud data is the same as that of the points in the corresponding first frame of point cloud data. Based on the associated image coordinates, the angle value information of the points in the second, third, and N-1th frames of point cloud data can be obtained.

[0089] Secondly, referring to FIG6B , in the example diagram shown in FIG6B , the point cloud data obtained by the laser radar every 1 / 2 circle rotation is regarded as a frame of point cloud data. In a specific implementation, a certain 1 / 2 circle during the stable scanning of the laser radar can be set as the first frame of point cloud data, and so on. The data obtained from the corresponding second 1 / 2 circle, third 1 / 2 circle, and up to the N-1th 1 / 2 circle are respectively regarded as the second frame, third frame, and up to the N-1th frame of point cloud data. When sending point cloud data, it is only necessary to obtain and transmit the angle value information contained in the first frame of point cloud data. Based on the coordinates and timestamp information of the associated image, the angle value information of the points in the second frame, third frame, and up to the N-1th frame of point cloud data can be calculated based on the angle value information contained in the first frame of point cloud data.

[0090] Similarly, referring to Figures 6C and 6D, in the example diagrams shown in Figures 6C and 6D, the point cloud data obtained every 1 / 4 or 1 / 8 of a rotation of the laser radar is regarded as a frame of point cloud data. In a specific implementation, the 1 / 4 or 1 / 8 of a rotation during the laser radar stable scanning can be set as the first frame of point cloud data. Similarly, the data obtained for the second 1 / 4 or 1 / 8 of a rotation, the third 1 / 4 or 1 / 8 of a rotation, and so on, are respectively regarded as the second frame, the third frame, and so on, and so on. When sending point cloud data, it is only necessary to obtain and transmit the angle value information contained in the first frame of point cloud data. Based on the coordinates and timestamp information of the associated image, the angle value information of the points in the second frame, the third frame, and so on, can be calculated based on the angle value information contained in the first frame of point cloud data.

[0091] It should be noted that the above examples are merely illustrative of frame point cloud data acquired by a LiDAR and are not intended to limit the embodiments of the present disclosure. Those skilled in the art can freely set the acquisition time for each frame, as long as the time length of different frames is the same. Furthermore, adjacent frames can be discontinuous and have a predetermined time interval, such as 0-300 milliseconds.

[0092] FIG7 is a schematic diagram of a structure of a laser radar point cloud data compression device 200 provided in an embodiment of the present disclosure. The laser radar point cloud data compression device 200 includes:

[0093] An acquisition module 201 is configured to acquire a first frame of point cloud data and a second frame of point cloud data from a laser radar, wherein the first frame of point cloud data includes depth value information, horizontal angle information, and vertical angle information of the point, and the second frame of point cloud data includes depth value information of the point;

[0094] an associating module 202 for associating the first frame of point cloud data, including the depth value information, horizontal angle information, and vertical angle information of the points, with the first image, and for associating the second frame of point cloud data, including the depth value information of the points, with the second image;

[0095] A compression module 203 compresses the first image and the second image to obtain a compressed image bit stream of the first frame of point cloud data and a compressed image bit stream of the second frame of point cloud data;

[0096] The sending module 204 is configured to transmit the compressed image bit stream of the first frame of point cloud data and the compressed image bit stream of the second frame of point cloud data.

[0097] The laser radar point cloud data compression and transmission device of the embodiment of the present disclosure collects point cloud data obtained by the laser radar so that the first bit stream of the first frame of point cloud data contains angle value information; for the subsequent second frame, third frame, and up to the N-1 frame of point cloud data, their angle value information can be inferred based on the angle information of the first frame of point cloud data, so that they do not contain angle value information, thereby further compressing the data volume.

[0098] As shown in FIG8 , a flowchart 300 of an example of compressing lidar point cloud data according to an embodiment of the present disclosure is shown, including:

[0099] Step 301: input a frame of laser radar point cloud data;

[0100] Step 302: Determine whether the point cloud data is the first frame data. If it is the first frame data, proceed to step 303; if not, proceed to step 305.

[0101] Step 303: Acquire the first frame of point cloud data, including depth value information, horizontal angle information, and vertical angle information of the points, and associate it with the first image;

[0102] Step 304: compress the first image to obtain a first frame of point cloud data compressed image bit stream;

[0103] If it is determined in step 302 that the collected data is not the first frame, then proceed to step 305 to calculate the depth and other attribute information of the laser radar corresponding to the point cloud data and associate it with the second image;

[0104] Step 306: compress the second image to obtain a second frame of point cloud data compressed image bit stream;

[0105] Step 307: Send the first frame point cloud data compressed image bit stream and the second frame point cloud data compressed image bit stream.

[0106] It should be noted that the compressed image bit stream of the first frame of point cloud data and the compressed image bit stream of the second frame of point cloud data in the implementation manner of the present application can be sent at the same time or transmitted in time-sharing. The specific sending interval and method do not serve as a limitation to the implementation manner of the present disclosure.

[0107] As shown in FIG9 , a flowchart 400 of an example of decompression of lidar point cloud data according to an embodiment of the present disclosure is shown, including:

[0108] Step 401: receiving a frame of point cloud data compressed image;

[0109] Step 402, decompressing the image;

[0110] Step 403: Determine whether the image decompressed in step 402 has angle information. If yes, proceed to step 404; if no, proceed to step 406.

[0111] Step 404: Calculate the depth value, angle value, and other attribute information of the laser radar, such as intensity value information, from the decompressed image;

[0112] Step 405: The angle value information calculated in step 404 is stored to obtain an incremental update angle value matrix, and the angle value is updated;

[0113] If it is determined in step 403 that the collected data is not the first frame of point cloud data, the process proceeds to step 406 to calculate the depth and other attribute information of the laser radar corresponding to the point cloud data; then, the process proceeds to step 407 to obtain the angle value of the frame of point cloud compressed image based on the updated angle value obtained in step 405;

[0114] Step 408: Repeat the above steps 401 to 407.

[0115] FIG10 is a schematic diagram 500 of a structure of a laser radar point cloud data decompression device according to an embodiment of the present disclosure, comprising:

[0116] A receiving module 501 is configured to receive a first compressed image bit stream and a second compressed image bit stream;

[0117] A decompression module 502, configured to decompress the first image and the second image;

[0118] Processing module 503 is configured to obtain, from the first image, depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data, as well as corresponding intensity value information of the laser radar; and to obtain, from the second image, depth value information of points included in the second frame of point cloud data, as well as corresponding intensity value information of the laser radar;

[0119] The storage module 504 is configured to store the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data;

[0120] The calling module 505 is configured to call the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data from the storage module 504, and obtain the horizontal angle information and the vertical angle information of the corresponding points in the second frame of point cloud data;

[0121] The horizontal angle information and the vertical angle information of the points included in the second frame of point cloud data can be calculated based on the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data.

[0122] As shown in FIG11 , a flowchart 600 of a method for constructing a point cloud of a laser radar according to an embodiment of the present disclosure is shown, including:

[0123] Step 601: receiving a frame of point cloud data compressed image;

[0124] Step 602, decompressing the image;

[0125] Step 603: Determine whether the image decompressed in step 602 contains angle information. If yes, proceed to step 604; if no, proceed to step 606.

[0126] Step 604: Calculate the depth value, angle value, and other attribute information of the laser radar, such as intensity value information, from the decompressed image;

[0127] Step 605: The angle value information calculated in step 604 is stored to obtain an incremental update angle value matrix, and the angle value is updated;

[0128] If it is determined in step 603 that the decompressed image does not contain angle information, the process proceeds to step 606 to calculate the depth and other attribute information of the laser radar corresponding to the point cloud data; then, the process proceeds to step 607 to obtain the angle value information of the frame of the compressed point cloud image based on the updated angle value obtained in step 605;

[0129] Step 608 : Repeat steps 601 to 607 until the angle value information, intensity information, and other attribute information of all point cloud data are calculated to construct the final point cloud.

[0130] As shown in FIG12 , a schematic diagram 700 of a point cloud construction device for a laser radar according to an embodiment of the present disclosure is shown, including:

[0131] A receiving module 701 is configured to receive a first compressed image bit stream and a second compressed image bit stream;

[0132] A decompression module 702, configured to decompress the first image and the second image;

[0133] Processing module 703 is configured to obtain, from the first image, depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data, as well as corresponding intensity value information of the laser radar; and to obtain, from the second image, depth value information of points included in the second frame of point cloud data, as well as corresponding intensity value information of the laser radar;

[0134] The storage module 704 is configured to store the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data;

[0135] The calling module 705 is configured to call the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data from the storage module 704, and obtain the horizontal angle information and the vertical angle information of the corresponding points in the second frame of point cloud data;

[0136] Construction module 706 is used to construct a final point cloud based on the depth value information, horizontal angle information and vertical angle information of the points included in the first frame point cloud data, and the corresponding intensity value information of the laser radar, as well as the depth value information of the points included in the second frame point cloud data, the corresponding intensity value information of the laser radar, and the horizontal angle information and vertical angle information of the corresponding points obtained in the second frame point cloud data.

[0137] FIG13 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. The electronic device 800 includes a transmitting unit 801, a processor 802, a storage unit 803, and a receiving unit 804. The transmitting unit 801 is used to transmit lidar point cloud data to be compressed or decompressed; the receiving unit 804 is used to receive lidar point cloud data to be compressed or decompressed; the processor 802 is used to compress or decompress the lidar point cloud data, and the storage unit 803 is used to store the data.

[0138] It should be noted that the division of units in the embodiments of the present disclosure is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0139] The present disclosure also provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the program implements the various steps of the embodiment of the above-described method for compressing lidar point cloud data, and can achieve the same technical effect. To avoid repetition, it is not described here. The readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NAND FLASH), solid-state drives (SSDs), etc.).

[0140] The embodiments of the present disclosure are described above in conjunction with the accompanying drawings, but the above-mentioned specific implementation methods are merely illustrative, and the present disclosure is not limited to the above-mentioned specific implementation methods. Under the guidance of the embodiments of the present disclosure, those skilled in the art may make various modifications and improvements without departing from the scope of protection of the embodiments of the present disclosure and the claims, and such modifications and improvements are also within the scope of protection of the present disclosure.

Claims

1. A method for compressing and transmitting laser radar point cloud data, characterized in that: include: Acquire a first frame of point cloud data of the laser radar, wherein the first frame of point cloud data includes depth value information, horizontal angle information, and vertical angle information of the point; Associating depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data with the first image to obtain a compressed image bit stream of the first frame of point cloud data; Acquire a second frame of point cloud data of the laser radar, where the second frame of point cloud data includes depth value information of points; Associating the second frame of point cloud data including the depth value information of the points to the second image to obtain a compressed image bit stream of the second frame of point cloud data; Among them, the number of rows of the first image and the second image respectively correspond to the number of lines of the laser radar, the number of columns of the first image and the second image respectively correspond to the number of single-circle scanning times of the laser radar, and the horizontal angle information and vertical angle information of the points included in the second frame of point cloud data can be calculated based on the horizontal angle information and vertical angle information of the points included in the first frame of point cloud data.

2. The method according to claim 1, characterized in that: The time lengths of the first frame of point cloud data and the second frame of point cloud data are the same.

3. The method according to claim 1, characterized in that There is a preset time interval between the first frame of point cloud data and the second frame of point cloud data.

4. The method according to claim 1, characterized in that The laser radar has 32 lines, 64 lines or 128 lines, and the laser radar has a single-circle scanning frequency greater than 500.

5. The method according to claim 1, characterized in that The first image includes at least one multi-channel image, and the number of channels of the multi-channel image corresponds to the type of data included in the first frame of point cloud data.

6. The method according to claim 1, characterized in that The first image includes a plurality of single-channel images, and the number of the single-channel images corresponds to the type of data included in the first frame of point cloud data.

7. The method according to claim 1, characterized in that The second image includes at least one multi-channel image, and the number of channels of the multi-channel image corresponds to the type of data included in the second frame of point cloud data.

8. The method according to claim 1, characterized in that: The second image includes a plurality of single-channel images, and the number of the single-channel images corresponds to the type of data included in the second frame of point cloud data.

9. The method according to any one of claims 5 to 8, characterized in that The data types include at least the depth value information of the point, the horizontal emission angle information, the vertical emission angle information, and the intensity value information of the laser radar.

10. A method for decompressing laser radar point cloud data, characterized in that: include: Receive a first compressed image bit stream, and obtain depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data, as well as the corresponding intensity value information of the laser radar; Storing horizontal angle information and vertical angle information of points included in the first frame of point cloud data; Receive a second compressed image bit stream, and obtain depth value information of points included in the second frame of point cloud data, and corresponding intensity value information of the laser radar; Among them, the number of rows of the first compressed image and the second compressed image respectively correspond to the number of lines of the laser radar, the number of columns of the first compressed image and the second compressed image respectively correspond to the number of single-circle scanning times of the laser radar, and the horizontal angle information and vertical angle information of the points of the second frame point cloud data can be calculated based on the horizontal angle information and vertical angle information of the points of the first frame point cloud data.

11. A point cloud construction method, characterized in that: include: Receive a first compressed image bit stream, and obtain depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data, as well as intensity value information of corresponding laser radar; Storing horizontal angle information and vertical angle information of points included in the first frame of point cloud data; Receive a second compressed image bit stream, and obtain depth value information of points included in the second frame of point cloud data, and corresponding intensity value information of the laser radar; wherein the number of rows of the first compressed image and the second compressed image respectively corresponds to the number of lines of the laser radar, the number of columns of the first compressed image and the second compressed image respectively corresponds to the number of single-circle scans of the laser radar, and the horizontal direction angle information and the vertical direction angle information of the points included in the second frame of point cloud data can be calculated based on the horizontal direction angle information and the vertical direction angle information of the points in the first frame of point cloud data; Recalling the stored horizontal angle information and vertical angle information of the points included in the first frame of point cloud data, and acquiring the horizontal angle information and vertical angle information of the corresponding points of the second frame of point cloud data; The final point cloud is constructed based on the depth value information, horizontal angle information and vertical angle information of the points included in the first frame of point cloud data, and the corresponding intensity value information of the laser radar, as well as the depth value information of the points included in the second frame of point cloud data, the corresponding intensity value information of the laser radar, and the horizontal angle information and vertical angle information of the corresponding points acquired in the second frame of point cloud data.

12. A laser radar point cloud data compression and transmission device, characterized in that: include: An acquisition module, used to acquire a first frame of point cloud data and a second frame of point cloud data of a laser radar, wherein the first frame of point cloud data includes depth value information, horizontal angle information, and vertical angle information of a point, and the second frame of point cloud data includes depth value information of a point; A processing module, configured to associate the first frame of point cloud data including depth value information, horizontal angle information, and vertical angle information of points with a first image to obtain a compressed image bit stream of the first frame of point cloud data; and used to associate the second frame of point cloud data including the depth value information of the points with the second image to obtain a compressed image bit stream of the second frame of point cloud data; a transmission module, configured to transmit a compressed image bit stream of the first frame of point cloud data and a compressed image bit stream of the second frame of point cloud data, Among them, the number of rows of the first image and the second image respectively corresponds to the number of lines of the laser radar, the number of columns of the first image and the second image respectively corresponds to the number of single-circle scanning times of the laser radar, and the horizontal angle information and vertical angle information of the points included in the second frame point cloud data can be calculated based on the horizontal angle information and vertical angle information of the points included in the first frame point cloud data.

13. A laser radar point cloud data decompression device, characterized in that: include: A receiving module, configured to receive a first compressed image bit stream and a second compressed image bit stream; a processing module, configured to obtain, from the first compressed image bit stream, the depth value information, the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data, and the corresponding intensity value information of the laser radar; and to obtain, from the second compressed image bit stream, the depth value information of the points included in the second frame of point cloud data, and the corresponding intensity value information of the laser radar; A storage module, used to store horizontal angle information and vertical angle information of points included in the first frame of point cloud data; A calling module, configured to call the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data from the storage module, and obtain the horizontal angle information and the vertical angle information of the corresponding points of the second frame of point cloud data; Among them, the number of rows of the first compressed image and the second compressed image respectively correspond to the number of lines of the laser radar, the number of columns of the first compressed image and the second compressed image respectively correspond to the number of single-circle scanning times of the laser radar, and the horizontal angle information and vertical angle information of the points included in the second frame of point cloud data can be calculated based on the horizontal angle information and vertical angle information of the points included in the first frame of point cloud data.

14. A point cloud construction device, characterized in that: include: A receiving module, configured to receive a first compressed image bit stream and a second compressed image bit stream; a processing module, configured to obtain, from the first compressed image bit stream, depth value information, horizontal angle information, and vertical angle information of points included in the first frame of point cloud data, and corresponding intensity value information of the laser radar; and to obtain, from the second compressed image bit stream, depth value information of points included in the second frame of point cloud data, and corresponding intensity value information of the laser radar; A storage module, used to store horizontal angle information and vertical angle information of points included in the first frame of point cloud data; A calling module, configured to call the horizontal angle information and the vertical angle information of the points included in the first frame of point cloud data from the storage module, and obtain the horizontal angle information and the vertical angle information of the corresponding points of the second frame of point cloud data; A construction module is used to construct a final point cloud according to the depth value information, horizontal angle information and vertical angle information of the points included in the first frame of point cloud data, and the corresponding intensity value information of the laser radar, as well as the depth value information of the points included in the second frame of point cloud data, the corresponding intensity value information of the laser radar, and the horizontal angle information and vertical angle information of the corresponding points of the acquired second frame of point cloud data, Among them, the number of rows of the first compressed image and the second compressed image respectively correspond to the number of lines of the laser radar, the number of columns of the first compressed image and the second compressed image respectively correspond to the number of single-circle scanning times of the laser radar, and the horizontal angle information and vertical angle information of the points included in the second frame of point cloud data can be calculated based on the horizontal angle information and vertical angle information of the points in the first frame of point cloud data.

15. An electronic device, characterized in that: include: one or more processors; A storage unit, used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the compression transmission method of the laser radar point cloud data according to any one of claims 1 to 9, and / or the decompression method of the laser radar point cloud data according to claim 10, and / or the point cloud construction method according to claim 11.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the compression transmission method of the laser radar point cloud data according to any one of claims 1 to 9, and / or the decompression method of the laser radar point cloud data according to claim 10, and / or the point cloud construction method according to claim 11.

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