WO2024087454A1 - Procédé et appareil de traitement de données d'un nuage de points laser, support de stockage et dispositif - Google Patents

Procédé et appareil de traitement de données d'un nuage de points laser, support de stockage et dispositif Download PDF

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Publication number
WO2024087454A1
WO2024087454A1 PCT/CN2023/080245 CN2023080245W WO2024087454A1 WO 2024087454 A1 WO2024087454 A1 WO 2024087454A1 CN 2023080245 W CN2023080245 W CN 2023080245W WO 2024087454 A1 WO2024087454 A1 WO 2024087454A1
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Prior art keywords
point cloud
cloud data
laser point
bit
laser
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PCT/CN2023/080245
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English (en)
Chinese (zh)
Inventor
施云飞
蒋成
周鹏
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上海易澳科技有限公司
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Priority to AU2023258364A priority Critical patent/AU2023258364B2/en
Publication of WO2024087454A1 publication Critical patent/WO2024087454A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/41—Bandwidth or redundancy reduction

Definitions

  • the present application relates to the field of image processing technology, and in particular to a method, device, storage medium and equipment for processing laser point cloud data.
  • the current image-based laser point cloud data processing method can compress a frame of laser point cloud data into an image. For each pixel point in a frame of image, it is necessary to store multiple data such as the depth value, azimuth angle and pitch angle of the laser point cloud data. Usually, a multi-channel image or multiple single-channel images are used to store this information. The pixel value of each channel in the multi-channel image or multiple single-channel images stores the quantized data of the laser point cloud data at the corresponding row and column positions, as shown in Figure 1. Finally, the image is compressed to achieve the purpose of compression.
  • the present application provides a laser point cloud data processing method, device, storage medium and equipment, which are used to solve the problem that when the azimuth angle and pitch angle information are stored separately as an image of one channel, the laser point cloud data has redundant data when it is compressed.
  • the technical solution is as follows:
  • a method for processing laser point cloud data comprising:
  • n columns of pixels in each image store the depth information of a frame of laser point cloud data
  • one of the remaining two columns of pixels stores the azimuth information of a frame of laser point cloud data
  • the azimuth information is the horizontal offset angle of one column of laser point cloud data
  • each horizontal offset angle is used to calculate the horizontal laser emission angle of the laser radar when measuring the corresponding row of laser point cloud data
  • the horizontal laser emission angle represents the azimuth angle of the laser point cloud data
  • another column of pixels stores the pitch angle information of a frame of laser point cloud data
  • the pitch angle information is the vertical laser emission angle when the laser radar measures a frame of laser point cloud data
  • the vertical laser emission angle represents the pitch angle of the laser point cloud data
  • the k images are compressed to obtain a compressed file.
  • the pitch angle information is the vertical laser emission angle of the qth column of laser point cloud data
  • compressing the k images to obtain a compressed file includes:
  • the k high-bit 8-bit images and the k low-bit 8-bit images are compressed to obtain the compressed file.
  • the method further includes:
  • the normalized data is quantized to obtain the depth information.
  • the line number m of the laser radar is 16 or 32 or 64 or 128;
  • the number n of periodic measurement points in the horizontal direction is a value set in the control system of the laser radar.
  • a laser point cloud data processing device comprising:
  • the acquisition module is used to obtain the number of laser radar lines m and the number of periodic measurement points n in the horizontal direction, where m ⁇ 2 and n ⁇ 2;
  • the acquisition module is further used to acquire k frames of laser point cloud data measured by the laser radar, where k ⁇ 1;
  • a generation module used for generating k images with m rows and n+2 columns according to m, n and the k frames of laser point cloud data, wherein the n columns of pixels in each image store the depth information of a frame of laser point cloud data, and one of the remaining two columns of pixels stores the azimuth information of a frame of laser point cloud data, wherein the azimuth information is the horizontal offset angle of one column of laser point cloud data, and each horizontal offset angle is used to calculate the horizontal laser emission angle of the laser radar when measuring the laser point cloud data of the corresponding row, and the horizontal laser emission angle represents the azimuth angle of the laser point cloud data; the other column of pixels stores the pitch angle information of a frame of laser point cloud data, and the pitch angle information is the vertical laser emission angle when the laser radar measures a frame of laser point cloud data, and the vertical laser emission angle represents the pitch angle of the laser point cloud data;
  • the compression module is used to compress the image to obtain a compressed file.
  • the pitch angle information is the vertical laser emission angle of the qth column of laser point cloud data
  • the compression module when each pixel stores 16 bits of data, the compression module is further used to:
  • the k high-bit 8-bit images and the k low-bit 8-bit images are compressed to obtain the compressed file.
  • a computer-readable storage medium wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the laser point cloud data processing method as described above.
  • a computer device which includes a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the laser point cloud data processing method as described above.
  • the number of periodic measurement points n in the horizontal direction and k frames of laser point cloud data, k images with m rows and n+2 columns are generated.
  • the n columns of pixels in each image store the depth information of one frame of laser point cloud data, and one column of pixels in the remaining two columns stores the azimuth information of one frame of laser point cloud data, and the other column of pixels stores the elevation information of one frame of laser point cloud data.
  • the depth value, azimuth angle and elevation angle of each frame of laser point cloud data can be stored in a single-channel image, avoiding the redundant data caused by storing the azimuth angle and the elevation angle separately as an image of one channel, thereby improving the compression efficiency.
  • each 16-bit data stored in the pixel can be split into high 8-bit data and low 8-bit data, and all high 8-bit data can be combined into k high 8-bit images, and all low 8-bit data can be combined into k low 8-bit images.
  • the k high 8-bit images and k low 8-bit images are compressed to obtain a compressed file, thereby further reducing the image size and improving the compression efficiency.
  • FIG1 is a schematic diagram of converting laser point cloud data into a multi-channel image
  • FIG2 is a schematic diagram of laser emission of a mechanical rotating laser radar
  • FIG3 is a schematic diagram of laser point cloud data projected onto an image
  • FIG4 is a flow chart of a method for processing laser point cloud data
  • FIG5 is a schematic diagram of mapping laser point cloud data to an image
  • FIG6 is a schematic diagram of splitting 16-bit data into high-bit data and low-bit data
  • FIG. 7 is a structural block diagram of a laser point cloud data processing device.
  • the mechanical rotating laser radar will emit a laser beam in a fixed angle direction, so that the scanned points are not random and disordered, but arranged at a certain angle in the horizontal and vertical directions.
  • the laser radar emits different laser beams in the vertical direction.
  • the different lines of these laser beams projected on the image correspond to the pitch angle of the laser beam; the laser radar is 360 degrees in the horizontal direction.
  • the laser radar evenly rotates and emits different laser beams, which are projected into different columns on the image, corresponding to the azimuth angles at which the laser beams are emitted.
  • the laser radar transmitter emits a laser beam, which is reflected back to the laser radar receiver after it touches the surface of the object.
  • the laser radar calculates the distance between the object and the laser radar based on the time difference between the emission time and the reception time of the laser beam, and stores this information in the corresponding pixel points of the image, as shown in Figure 3. Based on this feature, we can convert a frame of laser point cloud data scanned by the mechanical rotating laser radar into an image, thereby achieving the purpose of compressing the laser point cloud data.
  • Each pixel point of a frame of image needs to store data such as depth value, azimuth angle and pitch angle.
  • the depth value is mapped into an image
  • the azimuth angle is mapped into an image
  • the pitch angle is mapped into an image, as shown in Figure 1.
  • information such as azimuth angle and pitch angle needs to be stored separately as an image of one channel, which brings additional storage overhead and causes redundant data to exist in the laser point cloud data when it is compressed.
  • the direction of the laser beam emitted by the laser radar in the vertical direction is fixed.
  • the laser radar rotates 360 degrees at a constant speed around the rotation axis in the horizontal direction within a fixed time.
  • the direction of the emitted laser beam can be calculated based on the azimuth angle at a certain moment in the scanning process. Based on this scanning characteristic, we can further compress the azimuth angle and pitch angle scanned by the laser radar, and use a smaller storage space. We can still complete the compression and decompression processing of the laser point cloud data and restore the laser point cloud data collected by the laser radar.
  • FIG4 shows a method flow chart of a method for processing laser point cloud data provided by an embodiment of the present application.
  • the method for processing laser point cloud data can be applied to a computer device.
  • the method for processing laser point cloud data can include:
  • Step 401 obtaining the number of laser radar lines m and the number of periodic measurement points n in the horizontal direction.
  • the laser radar in this embodiment is a mechanical rotating laser radar.
  • the line number m means that the laser radar can emit m laser beams in the vertical direction, m ⁇ 2.
  • the line number m of the laser radar can be 16 or 32 or 64 or 128.
  • the number of periodic measurement points n in the horizontal direction refers to the number of laser beams that can be emitted by the laser radar during a 360-degree horizontal rotation, where n ⁇ 2.
  • the number of measurement points n is a value set in the control system of the laser radar. Users can manually set an appropriate value according to their needs.
  • m and n are both fixed values, and the computer device can store the obtained m and n for next use.
  • Step 402 obtaining k frames of laser point cloud data measured by the laser radar, where k ⁇ 1.
  • Step 403 generating k images with m rows and n+2 columns according to m, n and k frames of laser point cloud data, wherein the n columns of pixels in the image store the depth information of one frame of laser point cloud data, and one of the remaining two columns of pixels stores the azimuth information of one frame of laser point cloud data, the azimuth information is the horizontal offset angle of one column of laser point cloud data, each horizontal offset angle is used to calculate the horizontal laser emission angle of the laser radar when measuring the laser point cloud data of the corresponding row, and the horizontal laser emission angle represents the azimuth angle of the laser point cloud data; the other column of pixels stores the pitch angle information of one frame of laser point cloud data, the pitch angle information is the vertical laser emission angle when the laser radar measures one frame of laser point cloud data, and the vertical laser emission angle represents the pitch angle of the laser point cloud data.
  • the computer device can generate an image for each frame of laser point cloud data, and finally obtain k images.
  • the structure of each of the k images is the same. The following takes one of the images as an example to explain the data stored in the pixel points of the image.
  • An image has m rows and n+2 columns, where n columns of pixels store the depth information of a frame of laser point cloud data, one column of pixels stores the azimuth information of a frame of laser point cloud data, and one column of pixels stores the pitch information of a frame of laser point cloud data.
  • the first storage structure is that the first n columns of pixels store depth information, the n+1th column of pixels store azimuth information, and the n+2th column of pixels store pitch angle information; or, the first n columns of pixels store depth information, the n+1th column of pixels store pitch angle information, and the n+2th column of pixels store azimuth information.
  • the second storage structure is that any one column of pixels in the first n columns stores azimuth information, the n+2th column of pixels stores pitch information, and the remaining n columns of pixels store depth information; or, any one column of pixels in the first n columns stores pitch information, the n+2th column of pixels stores azimuth information, and the remaining n columns of pixels store depth information.
  • the third storage structure is that any two columns of pixels in the first n columns store azimuth information and pitch angle information respectively, and the remaining n columns of pixels store depth information.
  • Depth information is obtained by normalizing and quantizing the depth values of the laser point cloud data. Specifically, the depth values in the laser point cloud data are normalized to obtain normalized data; and the normalized data is quantized to obtain depth information. The purpose of the quantization process is to convert the normalized data into hexadecimal data.
  • the computer device For each point in a frame of laser point cloud data, the computer device first calculates the depth value d 1 of the point, multiplies the depth value d 1 by a suitable coefficient (normalization coefficient) to obtain the distance value d 2 , performs 16-bit row quantization processing on the distance value d 2 to obtain a distance value d 3 represented in 16-bit system, and stores the distance value d 3 in the pixel point of the corresponding row and column in the image.
  • a suitable coefficient normalization coefficient
  • the computer device first reads the distance value d 3 stored in the n-column pixel point, performs inverse quantization processing on the distance value d 3 to obtain the distance value d 2 , and divides the distance value d 2 by a suitable coefficient (normalization coefficient) to obtain the depth value d 1 .
  • the azimuth information is the horizontal offset angle of one column of laser point cloud data.
  • Each horizontal offset angle is used to calculate the horizontal laser emission angle of the laser radar when measuring the corresponding row of laser point cloud data.
  • the horizontal laser emission angle represents the azimuth angle of the laser point cloud data.
  • the row and column indexes of a frame of laser point cloud data are recorded as 1 ⁇ m and 1 ⁇ n, and the row and column indexes of the image are recorded as 0 ⁇ m-1 and 0 ⁇ n+1.
  • the computer device can map the azimuth information and pitch angle information of a frame of laser point cloud data to the rows and columns of the queue in the image according to the deviation of the two indexes and the storage structure mentioned above.
  • the azimuth information stores the horizontal position of the pth column of laser point cloud data.
  • 360/n represents the offset angle of each laser radar scan.
  • the pitch angle information is the vertical laser emission angle when the laser radar measures a frame of laser point cloud data.
  • the vertical laser emission angle represents the pitch angle of the laser point cloud data.
  • the pitch angle information is the vertical laser emission angle of the qth column of laser point cloud data
  • Step 404 compress the k images to obtain a compressed file.
  • the current effective detection distance of laser radar is about 200 meters, and the effective distance point actually used is within 100 meters.
  • centimeter-level accuracy 0.-20000cm corresponds to 0-65535.
  • most of the laser point cloud data measured by it is close-range, so the value of the lower 8 bits of the 16-bit data stored in each pixel is larger, and the value of the upper 8 bits is smaller.
  • Each 16-bit data stored in the pixel can be split into high 8-bit data and low 8-bit data, as shown in Figure 6, and then all high 8-bit data and all low 8-bit data are combined into an 8-bit single-channel image, and two 8-bit single-channel images are used to store high and low bit data respectively.
  • two 8-bit single-channel images we can use two 8-bit single-channel images to represent a 16-bit single-channel image, further reducing the size of the compressed image.
  • compressing k images to obtain a compressed file can include: splitting each 16-bit data in the k images into high 8-bit data and low 8-bit data; combining all high 8-bit data into k high 8-bit images, and combining all low 8-bit data into k low 8-bit images; compressing the k high 8-bit images and the k low 8-bit images to obtain a compressed file.
  • the computer device uses the imencode() function of OpenCV to compress k high-bit images and k low-bit images to obtain a binary data stream compressed file, and then packages and outputs the file.
  • decompressing the compressed file to obtain k images with m rows and n+2 columns can include: decompressing the compressed file to obtain k high-8-bit images and k low-8-bit images, each pixel in the high-8-bit image stores high-8-bit data of the 16-bit data, and each pixel in the low-8-bit image stores low-8-bit data of the 16-bit data; splicing each group of high-8-bit images and low-8-bit images into a 16-bit image to obtain m rows and n+2 columns. k images.
  • the computer device When decompressing the compressed file, the computer device uses the imencode() function of OpenCV to decompress the compressed file to obtain k high-bit single-channel images and k low-bit single-channel images. Then, the computer device concatenates the high-bit data and low-bit data stored in two pixels at the same position in a set of high-bit single-channel images and low-bit single-channel images into 16-bit data to obtain an image with m rows, n+2 columns, and 16-bit pixel content, where m ⁇ 2 and n ⁇ 2.
  • the computer device can calculate the coordinate value xyz of the laser point cloud data in the Cartesian coordinate system according to the depth value, azimuth angle and pitch angle to obtain the decompressed laser point cloud data.
  • the laser point cloud data processing method generates k images with m rows and n+2 columns according to the line number m of the laser radar, the number of periodic measurement points n in the horizontal direction and k frames of laser point cloud data.
  • the n columns of pixels in each image store the depth information of one frame of laser point cloud data, and one column of pixels in the remaining two columns stores the azimuth information of one frame of laser point cloud data, and the other column of pixels stores the pitch angle information of one frame of laser point cloud data.
  • the depth value, azimuth angle and pitch angle of each frame of laser point cloud data can be stored in a single-channel image, avoiding the redundant data caused by storing the azimuth angle and the pitch angle separately as an image of one channel, thereby improving the compression efficiency.
  • each 16-bit data stored in the pixel can be split into high 8-bit data and low 8-bit data, and all high 8-bit data can be combined into k high 8-bit images, and all low 8-bit data can be combined into k low 8-bit images.
  • the k high 8-bit images and k low 8-bit images are compressed to obtain a compressed file, thereby further reducing the image size and improving the compression efficiency.
  • FIG. 7 shows a block diagram of a laser point cloud data processing device provided by an embodiment of the present application.
  • the laser point cloud data processing device can be applied to a computer device.
  • the laser point cloud data processing device can include:
  • An acquisition module 710 is used to acquire the number of laser radar lines m and the number of periodic measurement points n in the horizontal direction, where m ⁇ 2 and n ⁇ 2;
  • the acquisition module 710 is also used to acquire k frames of laser point cloud data measured by the laser radar, where k ⁇ 1;
  • a generating module 720 is used to generate k images with m rows and n+2 columns according to m, n and k frames of laser point cloud data, wherein the n columns of pixels in each image store the depth information of one frame of laser point cloud data, and one of the remaining two columns of pixels stores the azimuth information of one frame of laser point cloud data, and the azimuth information is the horizontal offset angle of one column of laser point cloud data, and each horizontal offset angle is used to calculate the horizontal laser emission angle of the laser radar when measuring the laser point cloud data of the corresponding row, and the horizontal laser emission angle represents the azimuth angle of the laser point cloud data; another column of pixels stores the pitch angle information of one frame of laser point cloud data, and the pitch angle information is the vertical laser emission angle when the laser radar measures the laser point cloud data of one frame, and the vertical laser emission angle represents the pitch angle of the laser point cloud data;
  • the compression module 730 is used to compress k images to obtain a compressed file.
  • the pitch angle information is the vertical laser emission angle of the qth column of laser point cloud data
  • the compression module 730 is further configured to:
  • the generating module 720 is further configured to:
  • the normalized data is quantized to obtain depth information.
  • the number of laser radar lines m is 16 or 32 or 64 or 128;
  • the number of periodic measurement points n in the horizontal direction is a value set in the control system of the laser radar.
  • the laser point cloud data processing device generates k images with m rows and n+2 columns according to the line number m of the laser radar, the number of periodic measurement points n in the horizontal direction and k frames of laser point cloud data.
  • the n columns of pixels in each image store the depth information of one frame of laser point cloud data
  • one column of the remaining two columns of pixels stores the azimuth information of one frame of laser point cloud data
  • the other column of pixels stores the pitch angle information of one frame of laser point cloud data.
  • the depth value, azimuth angle and pitch angle of each frame of laser point cloud data can be stored in a single-channel image, avoiding the redundant data caused by storing the azimuth angle and the pitch angle separately as an image of one channel, thereby improving the compression efficiency.
  • each 16-bit data stored in the pixel can be split into high 8-bit data and low 8-bit data, and all high 8-bit data can be combined into k high 8-bit images, and all low 8-bit data can be combined into k low 8-bit images.
  • the k high 8-bit images and k low 8-bit images are compressed to obtain a compressed file, thereby further reducing the image size and improving the compression efficiency.
  • An embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the laser point cloud data processing method as described above.
  • An embodiment of the present application provides a computer device, which includes a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the laser point cloud data processing method as described above.
  • the program may be stored in a computer-readable storage medium.
  • the storage medium mentioned above may be a memory device.
  • the medium can be a read-only memory, a magnetic disk or an optical disk, etc.

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  • Physics & Mathematics (AREA)
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  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • General Physics & Mathematics (AREA)
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  • Remote Sensing (AREA)
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Abstract

Sont divulgués un procédé et un appareil de traitement de données d'un nuage de points laser, ainsi qu'un support de stockage et un dispositif, relevant du domaine technique du traitement d'images. Le procédé comprend les étapes consistant à : acquérir un nombre m de faisceaux et un nombre n de points de mesures périodiques dans une direction horizontale d'un radar laser (401) ; acquérir k trames de données d'un nuage de points laser mesurées par le radar laser (402) ; en fonction de m, de n et des k trames de données du nuage de points laser, générer k images ayant chacune un nombre m de lignes et un nombre n+2 de colonnes, n colonnes de points de pixels dans chaque image stockant des informations sur la profondeur d'une trame de données du nuage de points laser, une colonne de points de pixels parmi les deux colonnes de points de pixels restantes stockant des informations sur l'angle d'azimut d'une trame de données du nuage de points laser et l'autre colonne de points de pixels stockant des informations sur l'angle de pas d'une trame de données du nuage de points laser ; et compresser les k images de façon à obtenir un fichier compressé (404). La valeur de profondeur, l'angle d'azimut et l'angle de pas des données du nuage de points laser sont stockés dans une image à canal unique, ce qui permet d'éviter les données redondantes dues au stockage séparé de l'angle d'azimut et de l'angle de pas sous la forme d'une image à canal unique.
PCT/CN2023/080245 2022-10-25 2023-03-08 Procédé et appareil de traitement de données d'un nuage de points laser, support de stockage et dispositif WO2024087454A1 (fr)

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CN115586541A (zh) * 2022-10-25 2023-01-10 上海易澳科技有限公司 激光点云数据的处理方法、装置、存储介质及设备
CN116758174B (zh) * 2023-08-16 2023-11-10 北京易控智驾科技有限公司 激光雷达点云数据的压缩传输方法、装置及电子设备
CN117607829B (zh) * 2023-12-01 2024-06-18 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) 激光雷达点云的有序化重建方法、计算机可读存储介质

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CN114915790A (zh) * 2021-02-08 2022-08-16 荣耀终端有限公司 面向大规模点云的二维规则化平面投影及编解码方法
CN115035206A (zh) * 2022-05-09 2022-09-09 浙江华睿科技股份有限公司 一种激光点云的压缩方法、解压方法及相关装置
CN115586541A (zh) * 2022-10-25 2023-01-10 上海易澳科技有限公司 激光点云数据的处理方法、装置、存储介质及设备

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