WO2023179717A1 - 用于激光雷达的点云处理方法、装置、设备及存储介质 - Google Patents

用于激光雷达的点云处理方法、装置、设备及存储介质 Download PDF

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Publication number
WO2023179717A1
WO2023179717A1 PCT/CN2023/083413 CN2023083413W WO2023179717A1 WO 2023179717 A1 WO2023179717 A1 WO 2023179717A1 CN 2023083413 W CN2023083413 W CN 2023083413W WO 2023179717 A1 WO2023179717 A1 WO 2023179717A1
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Prior art keywords
point
points
point cloud
lidar
grid
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PCT/CN2023/083413
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English (en)
French (fr)
Inventor
王栋
陈森柯
夏冰冰
石拓
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北京一径科技有限公司
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Publication of WO2023179717A1 publication Critical patent/WO2023179717A1/zh

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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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

Definitions

  • the present disclosure relates to point cloud data processing technology in lidar, and in particular, to a point cloud processing method, device, equipment and storage medium for lidar.
  • lidar technology With the development of industrial intelligence, autonomous driving, robot obstacle avoidance, vehicle-road collaboration in smart cities, and surveying and mapping, there is an increasing demand for 3D sensing technology, especially lidar technology.
  • lidar for environmental perception, there is often a situation: due to the divergence angle of the emitted light of lidar, the light spot formed covers a certain area. When a certain light spot illuminates two objects in front and behind at the same time and interacts with each other, When the boundaries between objects are close together, the echoes generated will be superimposed. As shown in Figure 2, the dotted lines are the echoes formed at the boundaries of two objects that are close to each other when a light spot hits two objects at the same time, and the solid line is the actual superimposed echo signal.
  • embodiments of the present disclosure provide a point cloud processing method, device, equipment and storage medium for lidar.
  • a point cloud processing method for lidar including:
  • the sticking point processing includes: retaining points within a certain distance range of the closest point and the farthest point in the grid, and determining the remaining points as sticking points;
  • retaining points within a certain distance range of the closest point and the farthest point, and determining the remaining points as sticking points includes:
  • Points within the first set distance range of the nearest point and points within the second set distance range of the farthest point are retained, and the remaining points in the grid are determined as sticking points.
  • the method further includes:
  • the difference between the distance of the nearest point and the distance of the farthest point is calculated, and when the difference is greater than the first set threshold, sticky point processing is performed.
  • dividing the lidar point cloud into different grids includes:
  • the point cloud represented by the spherical coordinate system is divided into different grids; wherein the number of points in each grid is greater than or equal to a set value.
  • the method further includes:
  • the point cloud of the lidar is divided into different grids.
  • the method further includes:
  • the point cloud of lidar is divided into different grids, including:
  • a point cloud processing device for lidar including:
  • the division unit is used to divide the lidar point cloud into different grids according to the preset angle range and resolution;
  • a sticking point processing unit is used to perform sticking point processing; the sticking point processing includes: retaining point clouds within a certain distance range of the closest point and the farthest point, and determining the remaining point clouds as sticking points;
  • a deletion unit is used to delete the adhesion point.
  • the first determining unit is also used to:
  • the points within the first set distance range of the nearest point and the points within the second set distance range of the farthest point are retained, and the remaining points in the grid are determined as sticking points.
  • the device further includes:
  • a calculation unit configured to calculate the difference between the distance of the nearest point and the distance of the farthest point, and trigger the adhesion point processing unit to perform adhesion when the difference is greater than a first set threshold. Point processing.
  • the dividing unit is also used for:
  • the point cloud represented by the spherical coordinate system is rasterized; the number of point clouds in each grid is greater than or equal to the set value.
  • the dividing unit is also used for:
  • the point cloud of the lidar is divided into different grids.
  • the device further includes:
  • a second determination unit configured to determine the area whose distance is smaller than the second set threshold as the ROI of the point cloud
  • the dividing unit is also used to divide the point cloud contained in the ROI into different grids.
  • a computer-readable storage medium is provided.
  • a computer program is stored in the computer-readable storage medium.
  • the computer program is executed by a processor, the method for laser radar is implemented. Point cloud processing method steps.
  • an electronic device including:
  • a memory for storing instructions executable by a processor, wherein the processor is configured to execute the point cloud processing method for lidar when calling the executable instructions in the memory.
  • a computer program comprising computer readable code, which when run on a computing processing device causes the computing processing device to execute the method for laser Radar point cloud processing method.
  • the lidar point cloud is divided into different grids according to the preset angle range and resolution, all points in each grid are traversed, and the closest point and the farthest point in the grid are obtained. Points within a certain distance range of the nearest point and the farthest point are retained, the remaining points are determined as sticking points, and the sticking points are deleted. Since points containing adhesion phenomena are removed, obstacle size perception is more accurate based on the point cloud processed in the embodiment of the present disclosure.
  • embodiments of the present disclosure also support the first determination of possible In areas where adhesion occurs, only adhesion points are determined and deleted in areas where adhesion may occur, thereby improving point cloud processing efficiency.
  • Figure 1 shows a schematic diagram of the scene where point cloud data is adhered
  • Figure 2 shows a schematic diagram of the echo signal of the laser signal where adhesion occurs
  • Figure 3 shows a schematic diagram of the point cloud where adhesion occurs in the point cloud image
  • Figure 4 is a schematic flowchart of a point cloud processing method for lidar according to an embodiment of the present disclosure
  • Figure 5 is a schematic diagram of an example of a point cloud processing method for lidar according to an embodiment of the present disclosure
  • Figure 6 is a schematic distribution diagram of point cloud after rasterization according to an embodiment of the present disclosure.
  • Figure 7 shows a schematic diagram of the point cloud in which the sticking points have been deleted
  • Figure 8 is a schematic structural diagram of a point cloud processing device for lidar according to an embodiment of the present disclosure
  • FIG. 9 shows a configuration block diagram of an electronic device according to an embodiment of the present disclosure.
  • Figure 1 shows a schematic diagram of the scene where point cloud data is stuck.
  • the laser spot emitted by the lidar has a certain size
  • the distance is related to the width of the luminous pulse
  • the echoes generated will be superimposed together.
  • the distance is calculated based on the superimposed echo signals, which will lead to a discrepancy between the calculated result and the object.
  • There will be a huge deviation in the distance causing the size of the obstacle processed by the perception algorithm to deviate from the true value.
  • Figure 2 shows a schematic diagram of the echo signal of the laser signal where adhesion occurs.
  • the dotted line shows the echo signals formed at the boundaries of the objects that are close to each other when a beam of light spots irradiates two objects at the same time.
  • the solid line shows the actual echo signal after superposition.
  • the laser radar determines the distance to the object based on the superimposed echo signals shown in the solid line in Figure 2. When the distance is calculated, there will be a large deviation from the actual distance. In actual applications, if there are nearby obstacles in the direction of travel of the autonomous vehicle, it will affect the route planning of the autonomous vehicle and prevent normal passage.
  • Figure 3 shows a schematic diagram of the point cloud where adhesion occurs in the point cloud image. As shown in Figure 3, on the complete point cloud image, point cloud adhesion appears as floating point clouds appearing between the edges of the front and rear objects in the same direction.
  • FIG. 4 is a schematic flow chart of a point cloud processing method for lidar according to an embodiment of the present disclosure. As shown in Figure 4, the point cloud processing method for lidar according to an embodiment of the present disclosure includes the following steps:
  • Step 401 Divide the lidar point cloud into different grids according to the preset angle range and resolution.
  • the sticking points are searched for and deleted in the entire point cloud of the lidar.
  • the point cloud is rasterized according to the spherical coordinate system according to the preset angle range and resolution. Convert the lidar point cloud to a spherical coordinate system; rasterize the point cloud represented by the spherical coordinate system according to the preset angle range and resolution, where the points in each grid are The quantity is greater than or equal to the set value.
  • the purpose of dividing the point cloud into grids is to search for adhesive points based on the grid distribution characteristics of the point cloud. Since the grid contains a certain number of points, there is a high probability that the grid will contain points that illuminate the object in front, sticking points, and points that illuminate the object behind. Therefore, dividing it into grids can improve the accuracy of finding sticking points.
  • the setting value here can be set to 4, which ensures that the number of points in a sub-grid is maintained at about 4. Specifically, the setting value may also be 5, 6, etc. There are no limitations in the embodiments of this disclosure.
  • the size of the grid can be adjusted according to the preset angle range and the resolution of the point cloud data, so that the number of points in each grid is maintained. around the set value.
  • the point cloud of the lidar can be divided into different grids according to the coordinate information of the point cloud of the lidar. That is, determine which grid the point cloud falls into according to its coordinate value, and divide the point cloud into the corresponding grid.
  • the distribution angle range of the grid can be adjusted according to the resolution of the point cloud, so that the number of point clouds in each grid is ultimately maintained at the set number. value quantity.
  • Step 402 Traverse all points in each grid, obtain the closest point and the farthest point in the grid, and perform sticky point processing.
  • the sticking point processing includes: retaining points within a certain distance range of the closest point and the farthest point in the grid, and determining the remaining points as sticking points.
  • the ranging value of the point in each grid is obtained, and the farthest point and the closest point in the grid are determined.
  • Find the point cloud that is within the first set distance range from the nearest point, and points within a second set distance range from the farthest point, and points other than points within a certain distance range of the searched closest point and the farthest point are determined as the sticking points.
  • the first set distance range may be the same as the second set distance range.
  • the first set distance range can be set to a range of no more than 0.06m. Points within 0.06m of the nearest point in the grid are considered normal points, and the remaining points are adhesion points.
  • the second set distance range can be set to a range of no more than 0.06m. Points within a range of 0.06m near the farthest point in the grid are considered normal points, and the remaining points are adhesion points.
  • the first set distance range and the second set distance range may also be different.
  • the first set distance range can be set to a range of no more than 0.06m. Points within 0.06m of the nearest point in the grid are considered normal points, and the remaining points are adhesion points.
  • the second set distance range can be set to a range not exceeding 0.08m. When the point cloud within 0.08m near the farthest point in the grid is considered to be a normal point cloud, the remaining points are considered to be adhesive. point.
  • the above-mentioned first set distance range and second set distance range are only illustrative and not limiting.
  • the points in the normal grid that do not contain sticking points are usually within a certain distance range.
  • the first set threshold can be set to 0.4m. When the difference between the distance of the farthest point cloud in the grid and the distance of the nearest point cloud is greater than 0.4m, it is considered that there is a sticky point cloud in the grid.
  • Step 403 Delete the adhesion point.
  • the relevant areas where lidar may produce sticking points can be first determined, so that sticking points can be determined directly in the relevant areas, thereby saving computing resources for point cloud identification and improving the processing efficiency of sticking point identification.
  • the distance between the obstacle and the lidar is close, such as less than 1.6m, the pulse width of the lidar echo signal Wider, the probability that the laser echo signals of two closer obstacles will be superimposed increases, resulting in adhesion.
  • the pulse width of the echo that is far away is narrower, so the possibility of the echoes of two closer obstacles being superimposed will be reduced.
  • the area whose distance is smaller than the second set threshold is Determine the Region of Interest (ROI) as the point cloud, and the ROI is the area where sticking points are prone to occur.
  • the ROI area where adhesion points may occur is first determined, so that the search and deletion of adhesion points can only be performed in the ROI area. It is not necessary to search for adhesion points in all point clouds of the entire lidar, thus greatly improving the efficiency of the operation. Improve the efficiency of point cloud processing. Therefore, the ROI area of the point cloud can be selected based on distance or other areas that may produce sticking points.
  • the original point cloud of the lidar can be divided into ROI point cloud and non-ROI point cloud.
  • the second set threshold may be 1.6m. Those skilled in the art will understand that the second set threshold may also be other values such as 1.7m, 2.1m, etc., which are only exemplary.
  • the adhesion phenomenon that may be caused by obstacles is determined based on the distance of the point cloud. For example, when the point determined based on the echo signal is within a distance of less than 1.6m, the adhesion phenomenon of the point cloud is likely to occur. All the points can be In the collected point cloud, the area where the point distance is less than 1.6m is determined as the ROI area. In the embodiment of the present disclosure, the edge area of the obstacle may also be divided into ROI areas and the like according to the general shape of the obstacle.
  • Delete all the adhesion points found in the ROI area and use the lidar point cloud of the deletion of the adhesion points as the effective point cloud, that is, determine the point clouds included in the ROI after removing the adhesion points and the point clouds in non-ROI.
  • effective point cloud obstacle distance calculation and other data processing analysis are performed.
  • FIG. 5 is a schematic diagram of an example of a point cloud processing method for lidar according to an embodiment of the present disclosure. As shown in Figure 5, the point cloud processing method for lidar according to an embodiment of the present disclosure includes the following processing steps:
  • Step 1 Select the ROI area from the point cloud formed by all echo signals of the lidar.
  • the ROI area can be selected based on distance or other areas that may produce adhesion points.
  • the original point cloud is divided into ROI point cloud and non-ROI point cloud.
  • the ROI area can be the area corresponding to the point cloud within a certain distance (such as less than 1.6m).
  • Step 2 Rasterize the point cloud according to polar coordinates according to the preset angle range and resolution.
  • the back calculation is (element (polar angle), azimuth (azimuth angle), distance (distance)) of the spherical coordinate system.
  • all points in the point cloud are rasterized and divided into different grids according to the spherical coordinate system.
  • Rasterization refers to calculating the horizontal axis of the grid where each point is located based on the preset angular range and angular resolution.
  • the ordinate serial number is used to obtain the horizontal and vertical serial numbers (hori_pos and vert_pos) of the grid where each point is located.
  • hori_pos floor(azimuth-angle_hori_min)/angle_hori_resolution
  • angle_hori_min, angle_vert_min represent the minimum horizontal and vertical angle of the point to be processed
  • angle_hori_resolution represent the horizontal and vertical resolution of rasterization respectively
  • Floor(arg) is a downward rounding function, returning the largest integer not greater than arg value.
  • Step 3 calculate the closest point and the farthest point within each grid. Traverse all points in each grid and get the distance to the closest point and the distance to the farthest point.
  • Figure 6 is a schematic distribution diagram of the point cloud after rasterization processing according to an embodiment of the present disclosure.
  • the number 0 represents the divided grid unit
  • the number 1 represents the two objects before and after
  • 2 represents one type.
  • the grid size can be adjusted according to the radar scanning resolution (i.e., the spacing between points) to control the number of points in the grid.
  • the number of points in the grid should be at least 4. Since it contains a certain number of points, there is a high probability that the grid will contain points that illuminate the object in front, adhesion points, and points that illuminate the object behind; for example, label 3 contains points that illuminate the object in front, adhesion points, and points that illuminate the object behind.
  • a grid of points of the object behind; the grid represented by 3 has some points (shown as 1 in the figure) that just illuminate the boundary of the object before and after.
  • Step 4 Determine whether the difference between the nearest point and the farthest point in each grid is greater than the first set threshold. If the difference between the nearest point and the farthest point in the grid is greater than the first set threshold, perform step 5. Otherwise, It is considered that the grid does not contain sticky points, and the sticky point processing in step 5 is not performed.
  • Step 5 Determine the points in each grid that are within the first set distance range from the nearest point, and the points that are within the second set distance range from the farthest point.
  • the remaining points in the grid are regarded as Sticky points need to be deleted.
  • This processing method in the embodiment of the present disclosure will retain the points that illuminate the front obstacle and the points that illuminate the rear obstacle in the grid containing the sticking points, and the sticking points will be deleted.
  • the points in the normal grid are usually within a certain distance range; that is, the difference between the distance between the farthest point and the closest point is less than the above-mentioned first set threshold, and the above-mentioned method will not be executed.
  • Glue point deletion operation so that the normal point cloud can be retained.
  • Figure 7 shows a schematic diagram of the point cloud after deleting the sticking points, as shown in Figure 7 .
  • Step 6 Delete the sticky points in the grid.
  • the point cloud data of the lidar with the sticky points removed will be used as valid point cloud data.
  • Figure 8 is a schematic structural diagram of a point cloud processing device for lidar according to an embodiment of the present disclosure. As shown in Figure 8 As shown, the point cloud processing device for lidar in the embodiment of the present disclosure includes:
  • the dividing unit 80 is used to divide the lidar point cloud into different grids according to the preset angle range and resolution;
  • the acquisition unit 81 is used to traverse all point clouds in each grid and obtain the closest point and the farthest point in the grid;
  • the sticking point processing unit 82 is used to perform sticking point processing; the sticking point processing includes: retaining point clouds within a certain distance range of the closest point and the farthest point, and determining the remaining point clouds as sticking point clouds;
  • the deletion unit 83 is used to delete the adhesion point cloud.
  • the adhesion point processing unit 82 is also used to:
  • the points within the first set distance range of the nearest point and the points within the second set distance range of the farthest point are retained, and the remaining points in the grid are determined as sticking points.
  • the point cloud processing device for lidar according to the embodiment of the present disclosure also includes:
  • a calculation unit (not shown in Figure 8), used to calculate the difference between the distance of the nearest point and the distance of the farthest point, and trigger when the difference is greater than the first set threshold.
  • the sticking point processing unit performs sticking point processing.
  • the dividing unit 80 is also used to:
  • the point cloud represented by the spherical coordinate system is rasterized; the number of point clouds in each grid is greater than or equal to the set value.
  • the dividing unit 80 is also used to:
  • the point cloud of the lidar is divided into different grids.
  • the point cloud processing device for lidar according to the embodiment of the present disclosure also includes:
  • a determination unit (not shown in Figure 8), configured to determine the area whose distance is smaller than the second set threshold as the ROI of the point cloud;
  • the dividing unit 80 is also used to divide the point cloud contained in the ROI into different grids.
  • the dividing unit 80, the acquisition unit 81, the sticking point processing unit 82, the deletion unit 83, the calculation unit, the determination unit, etc. may be processed by one or more central processing units (CPUs, Central Processing Units), graphics Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC), DSP, Programmable Logic Device (PLD), Complex Programmable Logic Device (CPLD, Complex Programmable Logic Device), Field-Programmable Gate Array (FPGA, Field-Programmable Gate Array), general-purpose processor, controller, microcontroller (MCU, Micro Controller Unit), microprocessor (Microprocessor), or other electronic components.
  • CPUs Central Processing Units
  • GPU Graphics Processing Unit
  • ASIC Application Specific Integrated Circuit
  • DSP Digital Signal-Only Memory
  • PLD Programmable Logic Device
  • CPLD Complex Programmable Logic Device
  • FPGA Field-Programmable Gate Array
  • general-purpose processor controller, microcontroller (MCU, Micro Controller Unit), microprocessor (Microprocessor), or other
  • An embodiment of the present disclosure also describes an electronic device.
  • the electronic device includes: a processor and a memory for storing executable instructions by the processor, wherein the processor is configured to: when calling the executable instructions in the memory , the steps of the point cloud processing method for lidar of the embodiment can be performed.
  • Embodiments of the present disclosure also record a computer-readable storage medium.
  • a computer program is stored in the computer-readable storage medium.
  • the computer program is executed by a processor, the point cloud for laser radar of the embodiment is implemented. Processing method steps.
  • Embodiments of the present disclosure also record a computer program, including computer readable code.
  • the computer readable code When the computer readable code is run on a computing processing device, the computing processing device causes the computing processing device to perform the aforementioned point cloud processing for lidar. method.
  • FIG. 9 shows a configuration block diagram of an electronic device 900 according to an embodiment of the present disclosure.
  • Electronic device 900 may be any type of general or special purpose computing device, such as a desktop computer, laptop computer, server, mainframe computer, cloud-based computer, tablet computer, etc.
  • the electronic device 900 includes an input/output (I/O) interface 901 , a network interface 902 , a memory 904 and a processor 903 .
  • I/O input/output
  • I/O interface 901 is a collection of components that can receive input from and/or provide output to the user.
  • I/O interface 901 may include, but is not limited to, buttons, keyboards, keypads, LCD displays, LED displays, or other similar display devices, including display devices with touch screen capabilities enabling interaction between the user and the electronic device.
  • Network interface 902 may include various adapters and circuitry implemented in software and/or hardware to enable communication with the lidar system using wired or wireless protocols.
  • the wired protocol is, for example, any one or more of a serial port protocol, a parallel port protocol, an Ethernet protocol, a USB protocol or other wired communication protocols.
  • the wireless protocol is, for example, any IEEE802.11 Wi-Fi protocol, cellular network communication protocol, etc.
  • Memory 904 includes a single memory or one or more memories or storage locations, including but not limited to random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), read only memory (ROM) ), EPROM, EEPROM, flash memory, logic blocks of FPGA, hard disk, or any other layer of the memory hierarchy.
  • RAM random access memory
  • DRAM dynamic random access memory
  • SRAM static random access memory
  • ROM read only memory
  • EPROM electrically erasable programmable read only memory
  • EEPROM electrically erasable programmable read only memory
  • flash memory logic blocks of FPGA, hard disk, or any other layer of the memory hierarchy.
  • Processor 903 controls the general operation of electronic device 900 .
  • the processor 903 may include, but is not limited to, a CPU, a hardware microprocessor, a hardware processor, a multi-core processor, a single-core processor, a microcontroller, an application specific integrated circuit (ASIC), a DSP, or other similar processing device capable of executing Any type of instructions, algorithms, or software for controlling the operation and functionality of electronic device 900 of the embodiments described in this disclosure.
  • Processor 903 may be various implementations of digital circuitry, analog circuitry, or mixed-signal (a combination of analog and digital) circuitry that perform functions in a computing system.
  • Processor 903 may include, for example, a portion or circuit such as an integrated circuit (IC), a separate processor core, an entire processor core, a separate processor, a programmable hardware device such as a field programmable gate array (FPGA), and/or Systems that include multiple processors.
  • IC integrated circuit
  • FPGA field programmable gate array
  • Internal bus 906 may be used to establish communication between components of electronic device 900 .
  • the electronic device 900 is communicatively coupled to an autonomous vehicle including a lidar system to control the autonomous vehicle to avoid obstacles.
  • the point cloud processing method for lidar of the present disclosure may be stored on the memory 904 of the electronic device 900 in the form of computer-readable instructions.
  • the processor 903 implements the point cloud processing method for lidar by reading stored computer-readable instructions.
  • electronic device 900 is described using specific components, in alternative embodiments different components may be present in electronic device 900 .
  • electronic device 900 may include one or more additional processors, memory, network interfaces, and/or I/O interfaces. Additionally, one or more of the components may not be present in electronic device 900 . Additionally, although separate components are shown in FIG. 9 , in some embodiments some or all of a given component may be integrated into one or more of the other components in electronic device 900 .

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

一种用于激光雷达的点云处理方法、装置、设备及存储介质，该方法包括:根据预设的角度范围以及分辨率，将激光雷达的点云划分为不同的栅格（401）；遍历每个栅格内的所有点，获取栅格内最近点以及最远点，进行粘连点处理（402）；该粘连点处理包括:保留栅格内最近点以及最远点的一定距离范围内的点，将其余的点确定为粘连点；将该粘连点删除（403）。本方案处理后的点云数据更合理，能准确对障碍物进行避让，大大方便了自动驾驶中的路径规划，保证了行车安全。

Description

用于激光雷达的点云处理方法、装置、设备及存储介质
相关申请的交叉引用
本公开要求在2022年03月24日提交中国专利局、申请号为202210293400.0、名称为“用于激光雷达的点云处理方法及装置、存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开涉及激光雷达中点云数据处理技术，尤其涉及一种用于激光雷达的点云处理方法、装置、设备及存储介质。
背景技术
随着工业智能化的发展，自动驾驶、机器人避障、智慧城市的车路协同以及测绘领域等，对3D感知技术尤其是激光雷达技术的需求日益增加。在利用激光雷达进行环境感知的情况下，经常存在这样一种情况：由于激光雷达的发射光存在发散角，形成的光斑覆盖有一定面积，当某束光斑同时照射于前后两个物体且相互之间相距较近的物体边界处时，产生的回波就会叠加到一起。如图2所示，虚线为一束光斑同时打到前后两个物体且相互之间相距较近的物体边界处分别形成的回波，实线为实际叠加后的回波信号。由于不能区分前后两个物体分别形成的回波，实际信号处理中是根据叠加后的回波信号进行距离计算，计算与物体之间的距离会存在极大的偏差，因此会导致处于同方向上的前后物体边缘间出现漂浮的虚假点云，即出现点云粘连现象。对于自动驾驶车辆而言，若前方有两个障碍物相邻较近，很容易出现点云粘连现象，自动驾驶车辆根据出现粘连的点云进行感知，将会影响自动驾驶车辆的感知路线规划等算法，影响车速调整等发生。
发明内容
有鉴于此，本公开实施例提供一种用于激光雷达的点云处理方法、装置、设备及存储介质。
根据本公开实施例的第一方面，提供一种用于激光雷达的点云处理方法，包括：
根据预设的角度范围以及分辨率，将激光雷达的点云划分为不同的栅格；
遍历每个栅格内的所有点，获取栅格内最近点以及最远点，进行粘连点处理；
所述粘连点处理包括：保留栅格内最近点以及最远点的一定距离范围内的点，将其余的点确定为粘连点；
将所述粘连点删除。
在根据第一方面的一些示例性的实施例中，所述保留最近点以及最远点的一定距离范围内的点，将其余的点确定为粘连点，包括：
保留最近点的第一设定距离范围内的点以及最远点的第二设定距离范围内的点，将所述栅格内的其余的点确定为粘连点。
在根据第一方面的一些示例性的实施例中，所述方法还包括：
计算所述最近点的距离和所述最远点的距离之间的差值，在所述差值大于第一设定阈值的情况下，进行粘连点处理。
在根据第一方面的一些示例性的实施例中，所述将激光雷达的点云划分为不同的栅格，包括：
将所述激光雷达的点云转换为以球坐标系表征；
将所述球坐标系表征的点云划分为不同的栅格；其中，每个栅格中的点的数量大于或等于设定值。
在根据第一方面的一些示例性的实施例中，所述方法还包括：
根据所述激光雷达的点云的坐标信息，将所述激光雷达的点云划分至不同的栅格。
在根据第一方面的一些示例性的实施例中，所述方法还包括：
将激光雷达的点云中距离小于第二设定阈值的区域确定为感兴趣区域(Region of Interest，ROI)；
对应地，所述将激光雷达的点云划分为不同的栅格，包括：
将ROI包含的点云划分为不同的栅格。
根据本公开实施例的第二方面，提供一种用于激光雷达的点云处理装置，包括：
划分单元，用于根据预设的角度范围以及分辨率，将激光雷达的点云划分为不同的栅格；
获取单元，用于遍历每个栅格内的所有点云，获取栅格内最近点以及最远点；
粘连点处理单元，用于进行粘连点处理；所述粘连点处理包括：保留最近点以及最远点的一定距离范围内的点云，将其余的点云确定为粘连点；
删除单元，用于将所述粘连点删除。
在根据第二方面的一些示例性的实施例中，所述第一确定单元，还用于：
保留最近点的第一设定距离范围内的点，以及最远点的第二设定距离范围内的点，将栅格内的其余的点确定为粘连点。
在根据第二方面的一些示例性的实施例中，所述装置还包括：
计算单元，用于计算所述最近点的距离和所述最远点的距离之间的差值，在所述差值大于第一设定阈值的情况下，触发所述粘连点处理单元进行粘连点处理。
在根据第二方面的一些示例性的实施例中，所述划分单元，还用于：
将所述激光雷达的点云转换为以球坐标系表征；
将所述球坐标系表征的点云进行栅格化处理；其中，每个栅格中的点云的数量大于或等于设定值。
在根据第二方面的一些示例性的实施例中，所述划分单元，还用于：
根据所述激光雷达的点云的坐标信息，将所述激光雷达的点云划分至不同的栅格。
在根据第二方面的一些示例性的实施例中，所述装置还包括：
第二确定单元，用于将距离小于第二设定阈值的区域确定为点云的ROI；
对应地，所述划分单元，还用于：将ROI包含的点云划分为不同的栅格。
根据本公开实施例的第三方面，提供一种计算机可读存储介质，所述计算机可读存储介质内存储有计算机程序，所述计算机程序被处理器执行时实现所述的用于激光雷达的点云处理方法的步骤。
根据本公开实施例的第四方面，提供一种电子设备，所述电子设备包括：
处理器，和
用于存储处理器可执行指令的存储器，其中，所述处理器被配置为在调用存储器中的可执行指令时执行所述的用于激光雷达的点云处理方法。
根据本公开实施例的第五方面，提供一种计算机程序，包括计算机可读代码，当所述计算机可读代码在计算处理设备上运行时，导致所述计算处理设备执行所述的用于激光雷达的点云处理方法。
本公开实施例中，根据预设的角度范围以及分辨率，将激光雷达的点云划分为不同的栅格，遍历每个栅格内的所有点，获取栅格内最近点以及最远点，保留最近点以及最远点的一定距离范围内的点，将其余的点确定为粘连点，将所述粘连点删除。由于去除了包含有粘连现象的点，基于本公开实施例处理后的点云进行障碍物尺寸感知时更准确， 有利于自动驾驶车辆等真实的障碍物尺寸确定相应的路线规划，能准确对障碍物进行避让等，大大方便了自动驾驶中的路径规划，保证了行车安全；本公开实施例还支持先确定可能出现粘连的区域，仅对可能出现粘连的区域进行粘连点的确定并删除，从而提升点云处理效率。
附图说明
为了更清楚地说明本公开实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍。显而易见地，下面描述中的附图是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1示出了点云数据发生粘连的场景示意图；
图2示出了发生粘连的激光信号的回波信号示意图；
图3示出了点云图中发生粘连的点云示意图；
图4为本公开实施例的用于激光雷达的点云处理方法的流程示意图；
图5为本公开实施例的用于激光雷达的点云处理方法的示例示意图；
图6为本公开实施例的点云栅格化处理后的分布示意图；
图7示出了点云图中删除了粘连点的点云示意图；
图8为本公开实施例的用于激光雷达的点云处理装置的组成结构示意图；
图9示出根据本公开的实施例的电子设备的配置框图。
具体实施方式
以下结合附图，详细阐明本公开实施例技术方案的实质。
图1示出了点云数据发生粘连的场景示意图，如图1所示，由于激光雷达发射的激光光斑有一定大小，当一束激光光斑照射于前后两个物体且相互之间相距较近的物体边界处(该距离和发光脉冲宽度有关)，产生的回波就会叠加到一起，实际信号处理中是根据叠加后的回波信号进行距离计算，这样会导致所算结果与物体之间的距离会存在极大的偏差，导致感知算法处理得到的障碍物的尺寸偏离真实值。
图2示出了发生粘连的激光信号的回波信号示意图，如图2所示，虚线所示为一束光斑同时照射于前后两个物体且相距较近的物体边界处分别形成的回波信号，实线所示为实际叠加后的回波信号。激光雷达根据图2中实线所示叠加后的回波信号对物体的距 离进行计算时，将与实际距离存在较大的偏差。实际应用中如自动驾驶车辆行进方向存在相邻较近的障碍物，会影响自动驾驶车辆的路线规划，无法正常通行。
图3示出了点云图中发生粘连的点云示意图，如图3所示，在完整的点云图上，点云粘连表现为处于同方向上的前后物体边缘间会出现漂浮的点云。
图4为本公开实施例的用于激光雷达的点云处理方法的流程示意图，如图4所示，本公开实施例的用于激光雷达的点云处理方法包括以下步骤：
步骤401，根据预设的角度范围以及分辨率，将激光雷达的点云划分为不同的栅格。
本公开实施例中，为了避免激光雷达的点云中存在粘连点，对整个激光雷达的点云进行粘连点的查找并进行删除。
具体地，根据预设的角度范围以及分辨率，将点云按照球坐标系栅格化。将激光雷达的点云转换为以球坐标系表征；根据预设的角度范围以及分辨率，将所述球坐标系表征的点云进行栅格化处理，其中，每个栅格中的点的数量大于或等于设定值。本公开实施例中，将点云划分栅格的目的，是依据点云的分布特点按栅格进行粘连点的查找。由于栅格包含一定数量的点，从而使栅格内较大概率会包含照射在前面物体的点，粘连点和照射在后面物体的点，因此，划分为栅格可以提高粘连点的查找准确率及查找效率。这里的设定值可以设置为4，即保证一个子栅格中点的数量维持于4个左右。具体地，该设定值也可以为5、或6等。本公开实施例中不作限定。在栅格划分时，为保证每个栅格中的点数量，可以根据预设的角度范围以及点云数据的分辨率来调整栅格的大小，以使每个栅格中的点的数量维持在设定值左右。
本公开实施例中，可以根据所述激光雷达的点云的坐标信息，将所述激光雷达的点云划分至不同的栅格。即根据点云的坐标值确定其落入哪个栅格中，将该点云划分至相应栅格中。当栅格中的点云数量不满足上述设定数量的情况下，可以根据点云的分辨率，调整栅格的分配角度范围，最终使每个栅格内的点云数量均保持在设定值数量左右。
步骤402，遍历每个栅格内的所有点，获取栅格内最近点以及最远点，进行粘连点处理。
这里，所述粘连点处理包括：保留栅格内最近点以及最远点的一定距离范围内的点，将其余的点确定为粘连点。
本公开实施例中，针对所划分后的每个栅格，获取每个栅格中的点的测距值，确定栅格中的最远点和最近点。查找出与所述最近点距离位于第一设定距离范围内的点云， 以及与所述最远点距离位于第二设定距离范围内的点，将所查找的最近点以及最远点的一定距离范围内的点之外的点确定为所述粘连点。
本公开实施例中，第一设定距离范围可以与第二设定距离范围相同。例如，第一设定距离范围可以设置为不超过0.06m的范围，当栅格内的最近点的附近0.06m范围内的点，认为是正常点，其余的点则为粘连点。作为一种示例，第二设定距离范围可以设置为不超过0.06m的范围，当栅格内的最远点附近0.06m范围内的点，认为是正常点，其余的点则为粘连点。本领域技术人员应当理解，上述第一设定距离范围、第二设定距离范围仅为示例性说明，并非是限定。
本公开实施例中，第一设定距离范围与第二设定距离范围也可以不相同。例如，第一设定距离范围可以设置为不超过0.06m的范围，当栅格内的最近点的附近0.06m范围内的点，认为是正常点，其余的点则为粘连点。作为一种示例，第二设定距离范围可以设置为不超过0.08m的范围，当栅格内的最远点附近0.08m范围内的点云，认为是正常点云，其余的点则为粘连点。本领域技术人员应当理解，上述第一设定距离范围、第二设定距离范围仅为示例性说明，并非是限定。
本公开实施例中，由于在通常情况下，通过将点云划分为栅格，未包含粘连点的正常栅格中的点通常都处在一定距离范围内，为避免正常点的误删除，在确定粘连点的过程中，先计算所述最近点的距离和所述最远点的距离之间的差值，在所述差值大于第一设定阈值的情况下，将栅格内最近点以及最远点一定距离范围之外的点删除。也就是说，可以对栅格内的最近点和最远点之间的距离差值进行确定，只有在距离差值超过第一设定阈值的情况下，才认定该栅格区域内存在粘连点，再确定最近点第一设定距离范围内的点，以及最远点第二设定距离范围内的点，将所查找出的最近点及最远点设定距离范围之外的点均作为粘连点进行删除。如第一设定阈值可以设置为0.4m，当栅格中的最远点云的距离与最近点云的距离之间的差值大于0.4m的情况下，认为栅格中存在粘连点云。
步骤403，将所述粘连点删除。
本公开实施例中，为提升点云处理效率，还可以对可能出现点云粘连的区域进行预判断，将可能出现点云粘连的区域尽可能确定出，并将不同区域内的粘连点进行删除。
本公开实施例中，可以首先确定激光雷达可能产生粘连点的相关区域，以便直接在相关区域进行粘连点的判断，以节省点云识别的运算资源，提升粘连点识别的处理效率。当障碍物与激光雷达之间的距离较近如小于1.6m的情况下，激光雷达的回波信号的脉宽 较宽，两个距离较近的障碍物的激光回波信号叠加到一起的概率增加，导致产生粘连现象。而距离远的回波脉宽较窄，所以两个距离较近的障碍物的回波叠加到一起的可能性会降低，因此，本公开实施例中，将距离小于第二设定阈值的区域确定为点云的感兴趣区域(Region Of Interest，ROI)，ROI即为容易出现粘连点的区域。本公开实施例中，先确定出可能产生粘连点的ROI区域，以便仅在ROI区域中进行粘连点的查找及删除等处理，不必对整个激光雷达的所有点云进行粘连点的查找，从而大大提升点云处理的效率。因此，点云的ROI区域可以根据距离或者其他可能产生粘连点的区域进行选取，通过确定出点云的ROI区域，可以将激光雷达的原始点云分为ROI点云和非ROI点云。这里，第二设定阈值可以为1.6m，本领域技术人员应当理解，该第二设定阈值也可以是其他数值如1.7m、2.1m等，仅为示例性说明。
本公开实施例中，根据点云的距离确定障碍物可能导致的粘连现象，如当根据回波信号确定的点所在距离小于1.6m的范围内时，容易产生点云粘连的现象，可以将所采集的点云中，点距离小于1.6m的点所在区域均确定为ROI区域。本公开实施例中，也可以根据障碍物的大致形状，将障碍物的边缘区域划分为ROI区域等。
将ROI区域内所查找出的所有粘连点进行删除，将删除所述粘连点的激光雷达的点云作为有效点云，即将去除粘连点后的ROI包含的点云及非ROI中的点云确定为有效点云，进行障碍物距离运算及其他数据处理分析等。
以下通过具体示例，进一步阐明本公开实施例的技术方案的实质。
图5为本公开实施例的用于激光雷达的点云处理方法的示例示意图，如图5所示，本公开实施例的用于激光雷达的点云处理方法包括以下处理步骤：
步骤1，对激光雷达所有的回波信号形成的点云选取ROI区域，该ROI区域可以根据距离或者其他可能产生粘连点的区域选取，将原始点云分为ROI点云和非ROI点云。
由于距离远的回波脉宽较窄，所以两个物体的回波叠加到一起的可能性会降低，因此ROI区域可以为一定距离(如小于1.6m)范围内的点云对应的区域。
步骤2，根据预设的角度范围和分辨率对点云按极坐标栅格化。对于ROI区域的点云的每一个点云中的点云坐标(x，y，z)，反算为球坐标系的(element(极角)，azimuth(方位角)，distance(距离))，然后根据预设的角度范围以及分辨率，将点云中所有点按照球坐标系进行栅格化划分，划分为不同的栅格。
栅格化是指根据预设的角度范围和角度分辨率分别计算得到每个点所处的栅格的横 纵坐标序号，得到每个点所处栅格的水平和垂直序号(hori_pos和vert_pos)。
点云的栅格单元对应的坐标确定示例如下：
hori_pos＝floor(azimuth-angle_hori_min)/angle_hori_resolution
vert_pos＝floor(element-angle_vert_min)/angle_vert_resolution
其中，angle_hori_min,angle_vert_min，表示待处理点最小的水平和垂直角度；angle_hori_resolution，angle_vert_resolution分别表示栅格化水平和垂直的分辨率；Floor(arg)为向下取整函数，返回不大于arg的最大整数值。
步骤3，计算每个栅格内的最近点和最远点。遍历每个栅格内的所有点，得到最近点的距离以及最远点的距离。
图6为本公开实施例的点云栅格化处理后的分布示意图，如图6所示，图中，标号0表示划分后的栅格单元，标号1表示前后两个物体，2表示一种扫描方式的雷达的点云，可以根据雷达扫描分辨率(即点之间的间距)调整栅格大小，从而对栅格内点的数量进行控制，例如使栅格中点的数量至少为4，由于包含一定数量的点从而使栅格内较大概率会包含照射在前面物体的点，粘连点和照射于后面物体的点；例如标号3为包含照射于前面物体的点，粘连点和照射于后面物体的点的栅格；3表示的栅格有一些点(图中示意为1个)刚好照射于前后物体的边界上。
步骤4，判断每个栅格内最近点和最远点差值是否大于第一设定阈值，如果栅格内最近点和最远点差值大于第一设定阈值，执行步骤5，否则，认为该栅格内不包含粘连点，不执行步骤5的粘连点处理。
步骤5，确定每个栅格中与最近点的距离位于第一设定距离范围内的点，及与最远点的距离位于第二设定距离范围内的点，栅格中其余点视为粘连点，需要进行删除。
本公开实施例中的这种处理方式会将包含粘连点的栅格中照射于前面障碍物的点和照射于在后面障碍物的点保留，粘连点被删除。而对于未包含粘连点的正常栅格，正常栅格中的点通常都处在一定距离范围内；即最远点的距离和最近点的距离差值小于上述第一设定阈值，不执行上述粘连点删除操作，从而可以保留正常的点云。图7示出了删除了粘连点后的点云示意图，如图7所示。
步骤6，删除栅格中的粘连点。将删除粘连点的激光雷达的点云数据作为有效点云数据。
图8为本公开实施例的用于激光雷达的点云处理装置的组成结构示意图，如图8所 示，本公开实施例的用于激光雷达的点云处理装置包括：
划分单元80，用于根据预设的角度范围以及分辨率，将激光雷达的点云划分为不同的栅格；
获取单元81，用于遍历每个栅格内的所有点云，获取栅格内最近点以及最远点；
粘连点处理单元82，用于进行粘连点处理；所述粘连点处理包括：保留最近点以及最远点的一定距离范围内的点云，将其余的点云确定为粘连点云；
删除单元83，用于将所述粘连点云删除。
在一些示例性的实施例中，所述粘连点处理单元82，还用于：
保留最近点的第一设定距离范围内的点，以及最远点的第二设定距离范围内的点，将栅格内的其余的点确定为粘连点。
在图8所示的用于激光雷达的点云处理装置的基础上，本公开实施例的用于激光雷达的点云处理装置还包括：
计算单元(图8中未示出)，用于计算所述最近点的距离和所述最远点的距离之间的差值，在所述差值大于第一设定阈值的情况下，触发所述粘连点处理单元进行粘连点处理。
在一些示例性的实施例中，所述划分单元80，还用于：
将所述激光雷达的点云转换为以球坐标系表征；
将所述球坐标系表征的点云进行栅格化处理；其中，每个栅格中的点云的数量大于或等于设定值。
在一些示例性的实施例中，所述划分单元80，还用于：
根据所述激光雷达的点云的坐标信息，将所述激光雷达的点云划分至不同的栅格。
在图8所示的用于激光雷达的点云处理装置的基础上，本公开实施例的用于激光雷达的点云处理装置还包括：
确定单元(图8中未示出)，用于将距离小于第二设定阈值的区域确定为点云的ROI；
对应地，所述划分单元80，还用于：将ROI包含的点云划分为不同的栅格。
在示例性实施例中，划分单元80、获取单元81、粘连点处理单元82、删除单元83、计算单元、确定单元等可以被一个或多个中央处理器(CPU，Central Processing Unit)、图形处理器(GPU，Graphics Processing Unit)、应用专用集成电路(ASIC，Application Specific Integrated Circuit)、DSP、可编程逻辑器件(PLD，Programmable Logic Device)、 复杂可编程逻辑器件(CPLD，Complex Programmable Logic Device)、现场可编程门阵列(FPGA，Field-Programmable Gate Array)、通用处理器、控制器、微控制器(MCU，Micro Controller Unit)、微处理器(Microprocessor)、或其他电子元件实现。
在本公开实施例中，图8示出的用于激光雷达的点云处理装置中各个单元执行操作的具体方式已经在有关该方法的实施例中进行了详细描述，此处将不做详细阐述说明。
本公开实施例还记载了一种电子设备，所述电子设备包括：处理器和用于存储处理器可执行指令的存储器，其中，所述处理器被配置为在调用存储器中的可执行指令时，能够执行所述实施例的用于激光雷达的点云处理方法的步骤。
本公开实施例还记载了一种计算机可读存储介质，所述计算机可读存储介质内存储有计算机程序，所述计算机程序被处理器执行时实现所述实施例的用于激光雷达的点云处理方法的步骤。
本公开实施例还记载了一种计算机程序，包括计算机可读代码，当所述计算机可读代码在计算处理设备上运行时，导致所述计算处理设备执行前述的用于激光雷达的点云处理方法。
图9示出根据本公开的实施例的电子设备900的配置框图。电子设备900可为任何类型的通用或专用计算设备，诸如台式计算机、膝上型计算机、服务器、大型计算机、基于云的计算机、平板计算机等。如图9所示，电子设备900包括输入输出(Input/Output,I/O)接口901、网络接口902、存储器904和处理器903。
I/O接口901是可以从用户接收输入和/或向用户提供输出的组件的集合。I/O接口901可以包括但不限于按钮、键盘、小键盘、LCD显示器、LED显示器或其它类似的显示设备，包括具有触摸屏能力使得能够进行用户和电子设备之间的交互的显示设备。
网络接口902可以包括各种适配器以及以软件和/或硬件实现的电路系统，以便能够使用有线或无线协议与激光雷达系统通信。有线协议例如是串口协议、并口协议、以太网协议、USB协议或其它有线通信协议中的任何一种或多种。无线协议例如是任何IEEE802.11Wi-Fi协议、蜂窝网络通信协议等。
存储器904包括单个存储器或一个或多个存储器或存储位置，包括但不限于随机存取存储器(RAM)、动态随机存取存储器(DRAM)、静态随机存取存储器(SRAM)、只读存储器(ROM)、EPROM、EEPROM、闪存、FPGA的逻辑块、硬盘或存储器层次结构的任何其他各层。存储器904可以用于存储任何类型的指令、软件或算法，包括用于 控制电子设备900的一般功能和操作的指令905。
处理器903控制电子设备900的一般操作。处理器903可以包括但不限于CPU、硬件微处理器、硬件处理器、多核处理器、单核处理器、微控制器、专用集成电路(ASIC)、DSP或其他类似的处理设备，能够执行根据本公开中描述的实施例的用于控制电子设备900的操作和功能的任何类型的指令、算法或软件。处理器903可以是在计算系统中执行功能的数字电路系统、模拟电路系统或混合信号(模拟和数字的组合)电路系统的各种实现。处理器903可以包括例如诸如集成电路(IC)、单独处理器核心的部分或电路、整个处理器核心、单独的处理器、诸如现场可编程门阵列(FPGA)的可编程硬件设备、和/或包括多个处理器的系统。
可以使用内部总线906来建立电子设备900的组件之间的通信。
电子设备900通信耦接到包括激光雷达系统的自动驾驶车辆，以控制自动驾驶车辆对障碍物进行避让。例如，可以将本公开的用于激光雷达的点云处理方法以计算机可读指令的形式存储在电子设备900的存储器904上。处理器903通过读取所存储的计算机可读指令来实施用于激光雷达的点云处理方法。
尽管使用特定组件来描述电子设备900，但是在替选实施例中，电子设备900中可以存在不同的组件。例如，电子设备900可以包括一个或多个附加处理器、存储器、网络接口和/或I/O接口。另外，电子设备900中可能不存在组件的一个或多个。另外，尽管在图9中示出单独的组件，但是在一些实施例中，给定组件的一些或全部可以集成到电子设备900中的其他组件中的一个或多个中。
应理解，说明书通篇中提到的“一个实施例”或“一实施例”意味着与实施例有关的特定特征、结构或特性包括在本公开的至少一个实施例中。因此，在整个说明书各处出现的“在一个实施例中”或“在实施例中”未必一定指相同的实施例。此外，这些特定的特征、结构或特性可以任意适合的方式结合在一个或多个实施例中。应理解，在本公开的各种实施例中，上述各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本公开实施例的实施过程构成任何限定。上述本公开实施例序号仅仅为了描述，不代表实施例的优劣。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者 装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
在本公开所提供的几个实施例中，应该理解到，所揭露的设备和方法，可以通过其它的方式实现。以上所描述的设备实施例仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，如：多个单元或组件可以结合，或可以集成到另一个系统，或一些特征可以忽略，或不存在。
上述作为分离部件说明的单元可以是、或也可以不是物理上分开的，作为单元显示的部件可以是、或也可以不是物理单元；可以根据实际的需要选择其中的部分或全部单元来实现本实施例方案的目的。
以上所述，仅为本公开的实施方式，但本公开的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本公开揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本公开的保护范围之内。因此，本公开的保护范围应以所述权利要求的保护范围为准。

Claims (15)

  1. 一种用于激光雷达的点云处理方法，其特征在于，所述方法包括：
    根据预设的角度范围以及分辨率，将激光雷达的点云划分为不同的栅格；
    遍历每个栅格内的所有点，获取栅格内最近点以及最远点，进行粘连点处理；
    所述粘连点处理包括：保留栅格内最近点以及最远点的一定距离范围内的点，将其余的点确定为粘连点；
    将所述粘连点删除。
  2. 根据权利要求1所述的方法，其特征在于，所述保留最近点以及最远点的一定距离范围内的点，将其余的点确定为粘连点，包括：
    保留最近点的第一设定距离范围内的点以及最远点的第二设定距离范围内的点，将所述栅格内的其余的点确定为粘连点。
  3. 根据权利要求1或2所述的方法，其特征在于，所述方法还包括：
    计算所述最近点的距离和所述最远点的距离之间的差值，在所述差值大于第一设定阈值的情况下，进行粘连点处理。
  4. 根据权利要求1或2所述的方法，其特征在于，所述将激光雷达的点云划分为不同的栅格，包括：
    将所述激光雷达的点云转换为以球坐标系表征；
    将所述球坐标系表征的点云划分为不同的栅格；其中，每个栅格中的点的数量大于或等于设定值。
  5. 根据权利要求1或2所述的方法，其特征在于，所述方法还包括：
    根据所述激光雷达的点云的坐标信息，将所述激光雷达的点云划分至不同的栅格。
  6. 根据权利要求1或2所述的方法，其特征在于，所述方法还包括：
    将激光雷达的点云中距离小于第二设定阈值的区域确定为感兴趣区域ROI；
    对应地，所述将激光雷达的点云划分为不同的栅格，包括：
    将ROI包含的点云划分为不同的栅格。
  7. 一种用于激光雷达的点云处理装置，其特征在于，所述装置包括：
    划分单元，用于根据预设的角度范围以及分辨率，将激光雷达的点云划分为不同的栅格；
    获取单元，用于遍历每个栅格内的所有点云，获取栅格内最近点以及最远点；
    粘连点处理单元，用于进行粘连点处理；所述粘连点处理包括：保留最近点以及最远点的一定距离范围内的点云，将其余的点云确定为粘连点；
    删除单元，用于将所述粘连点删除。
  8. 根据权利要求7所述的装置，其特征在于，所述粘连点处理单元，还用于：
    保留最近点的第一设定距离范围内的点，以及最远点的第二设定距离范围内的点，将栅格内的其余的点确定为粘连点。
  9. 根据权利要求7或8所述的装置，其特征在于，所述装置还包括：
    计算单元，用于计算所述最近点的距离和所述最远点的距离之间的差值，在所述差值大于第一设定阈值的情况下，触发所述粘连点处理单元进行粘连点处理。
  10. 根据权利要求7或8所述的装置，其特征在于，所述划分单元，还用于：
    将所述激光雷达的点云转换为以球坐标系表征；
    将所述球坐标系表征的点云进行栅格化处理；其中，每个栅格中的点云的数量大于或等于设定值。
  11. 根据权利要求7或8所述的装置，其特征在于，所述划分单元，还用于：
    根据所述激光雷达的点云的坐标信息，将所述激光雷达的点云划分至不同的栅格。
  12. 根据权利要求7或8所述的装置，其特征在于，所述装置还包括：
    确定单元，用于将距离小于第二设定阈值的区域确定为点云的ROI；
    对应地，所述划分单元，还用于：将ROI包含的点云划分为不同的栅格。
  13. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质内存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1至6任一项所述的用于激光雷达的点云处理方法的步骤。
  14. 一种电子设备，其特征在于，所述电子设备包括：
    处理器，和
    用于存储处理器可执行指令的存储器，其中，所述处理器被配置为在调用存储器中的可执行指令时执行如权利要求1至6中任一项所述的用于激光雷达的点云处理方法。
  15. 一种计算机程序，其特征在于，包括计算机可读代码，当所述计算机可读代码在计算处理设备上运行时，导致所述计算处理设备执行根据权利要求1至6中任一项所述的用于激光雷达的点云处理方法。
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