WO2022017133A1 - Method and apparatus for processing point cloud data - Google Patents
Method and apparatus for processing point cloud data Download PDFInfo
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- WO2022017133A1 WO2022017133A1 PCT/CN2021/102856 CN2021102856W WO2022017133A1 WO 2022017133 A1 WO2022017133 A1 WO 2022017133A1 CN 2021102856 W CN2021102856 W CN 2021102856W WO 2022017133 A1 WO2022017133 A1 WO 2022017133A1
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Definitions
- the present disclosure relates to the technical field of information processing, and in particular, to a method and device for processing point cloud data.
- LiDAR is widely used in the fields of automatic driving, UAV exploration, map mapping and other fields with its precise ranging ability.
- autonomous driving in the application scenario of autonomous driving, the point cloud data collected by lidar is generally processed to realize the positioning of the vehicle and the identification of obstacles. It consumes a lot of computing resources.
- the calculation method of this calculation method Low efficiency and low utilization of computing resources.
- the embodiments of the present disclosure provide at least one point cloud data processing method and device.
- an embodiment of the present disclosure provides a point cloud data processing method, including: acquiring point cloud data to be processed obtained by scanning a radar device in a target scene; The target point cloud data is screened out from the point cloud data to be processed; the target point cloud data is detected to obtain a detection result.
- the point cloud data to be processed collected by the radar device in the target scene can be screened based on the effective perception range information corresponding to the target scene, and the screened target point cloud data is the target point cloud data corresponding to the target scene Therefore, based on the filtered point cloud data, the detection calculation is performed in the target scene, which can reduce the amount of calculation, improve the calculation efficiency, and the utilization rate of computing resources in the target scene.
- the effective perception range information corresponding to the target scene is determined according to the following methods: obtaining computing resource information of a processing device; based on the computing resource information, determining the Effective sensing range information.
- filtering out the target point cloud data from the to-be-processed point cloud data includes: determining an effective coordinate range based on the effective sensing range information. ; Based on the effective coordinate range, filter out the target point cloud data from the to-be-processed point cloud data.
- the determining the effective coordinate range based on the effective sensing range information includes: based on the position information of the reference position point within the effective sensing range, and the reference position point within the target The coordinate information in the scene determines the valid coordinate range corresponding to the target scene.
- the filtering out target point cloud data from the point cloud data to be processed based on the effective coordinate range includes: scanning a radar with the corresponding coordinate information located within the effective coordinate range. Points are used as radar scanning points in the target point cloud data.
- the coordinate information of the reference position point in the target scene is determined according to the following methods: obtaining the position information of the intelligent driving device on which the radar device is set; based on the position information of the intelligent driving device Determine the road type of the road where the intelligent driving device is located; and obtain the coordinate information of the reference position point matching the road type as the coordinate information of the reference position point in the target scene.
- the point cloud data that the intelligent driving device needs to process may be different when it is located on roads of different road types. Therefore, by obtaining the coordinate information of the reference position point matching the road type, the intelligent driving device can determine the current The valid coordinate range of the road type where it is located, so as to filter out the point cloud data under the corresponding road type, thereby improving the accuracy of the detection results of the intelligent driving device under different road types.
- the detection result includes the position of the object to be identified in the target scene; the detecting the target point cloud data to obtain the detection result includes: performing the detection on the target point cloud data Perform rasterization processing to obtain a grid matrix; the value of each element in the grid matrix is used to represent whether there is a target point at the corresponding grid; according to the grid matrix and the to-be-identified in the target scene
- the size information of the object is used to generate a sparse matrix corresponding to the object to be recognized; based on the generated sparse matrix, the position of the object to be recognized in the target scene is determined.
- the generating a sparse matrix corresponding to the to-be-identified object according to the grid matrix and the size information of the to-be-identified object in the target scene includes: according to the grid matrix and the size information of the object to be identified in the target scene, perform at least one expansion processing operation or erosion processing operation on the target element in the grid matrix, and generate a sparse matrix corresponding to the object to be identified; wherein, the The value of the target element indicates that the target point exists at the corresponding grid.
- the expansion processing operation or the erosion processing operation includes shift processing and logical operation processing, and the difference between the coordinate range of the sparse matrix and the size of the object to be identified is within a preset threshold. within the range.
- At least one expansion processing operation is performed on the elements in the grid matrix to generate a
- the sparse matrix corresponding to the object includes: performing a first inversion operation on the elements in the grid matrix before the current expansion processing operation to obtain the grid matrix after the first inversion operation;
- the grid matrix after the first inversion operation is subjected to at least one convolution operation to obtain a grid matrix with a preset sparsity after at least one convolution operation; the preset sparsity is determined by the target scene.
- the size information of the identification object is determined; the second inversion operation is performed on the elements in the grid matrix with the preset sparsity after the at least one convolution operation to obtain the sparse matrix.
- performing the first inversion operation on the elements in the grid matrix before the current expansion processing operation to obtain the grid matrix after the first inversion operation includes: based on the second preset A convolution kernel, which performs a convolution operation on other elements except the target element in the grid matrix before the current expansion processing operation, to obtain the first inversion element, and based on the second preset convolution kernel, performs a convolution operation on the current time
- the target element in the grid matrix before the expansion processing operation is subjected to a convolution operation to obtain a second inversion element; based on the first inversion element and the second inversion element, the grid after the first inversion operation is obtained. lattice matrix.
- At least one convolution operation is performed on the grid matrix after the first inversion operation based on the first preset convolution check, to obtain at least one convolution operation with a preset sparsity.
- the grid matrix includes: for the first convolution operation, performing a convolution operation on the grid matrix after the first inversion operation and the first preset convolution kernel to obtain the grid after the first convolution operation. matrix; repeat the steps of performing the convolution operation on the grid matrix after the previous convolution operation and the first preset convolution kernel to obtain the grid matrix after the current convolution operation, until obtaining the grid matrix with the pre-set convolution kernel. Set the raster matrix of sparsity.
- the first preset convolution kernel has a weight matrix and an offset corresponding to the weight matrix; for the first convolution operation, the grid after the first inversion operation is Performing a convolution operation on the lattice matrix and the first preset convolution kernel to obtain a lattice matrix after the first convolution operation, including: for the first convolution operation, according to the size of the first preset convolution kernel and the preset steps length, select each grid sub-matrix from the grid matrix after the first inversion operation; for each selected grid sub-matrix, multiply the grid sub-matrix and the weight matrix operation to obtain a first operation result, and adding the first operation result and the offset to obtain a second operation result; based on the second operation result corresponding to each of the grid sub-matrixes, determine the first volume The grid matrix after the product operation.
- At least one corrosion processing operation is performed on the elements in the grid matrix to generate a
- the sparse matrix corresponding to the object includes: performing at least one convolution operation on the grid matrix to be processed based on the third preset convolution kernel to obtain a grid matrix with a preset sparsity after at least one convolution operation;
- the sparsity is determined by the size information of the object to be identified in the target scene;
- the grid matrix with the preset sparsity after the at least one convolution operation is determined as the sparseness corresponding to the object to be identified matrix.
- performing grid processing on the target point cloud data to obtain a grid matrix includes: performing grid processing on the target point cloud data to obtain a grid matrix and the grid matrix.
- the correspondence between each element and the coordinate range information of each target point; the determining the position range of the object to be identified in the target scene based on the generated sparse matrix includes: based on the grid matrix The corresponding relationship between each element in the sparse matrix and the coordinate range information of each target point, determine the coordinate information of the target point corresponding to each target element in the generated sparse matrix; The coordinate information of the target point is combined to determine the position of the object to be recognized in the target scene.
- the determining the position of the object to be identified in the target scene based on the generated sparse matrix includes: pairing the generated sparse matrix based on a trained convolutional neural network. Perform at least one convolution process on each target element in to obtain a convolution result; based on the convolution result, determine the position of the object to be identified in the target scene.
- the method further includes: controlling and setting an intelligent driving device of the radar device based on the detection result.
- an embodiment of the present disclosure further provides a point cloud data processing device, including: an acquisition module for acquiring point cloud data to be processed obtained by scanning a radar device in a target scene; a screening module for according to the target The effective perception range information corresponding to the scene is used to filter out the target point cloud data from the to-be-processed point cloud data; the detection module is used to detect the target point cloud data to obtain a detection result.
- embodiments of the present disclosure further provide a computer device, including a processor, a memory, and a bus, where the memory stores machine-readable instructions executable by the processor, and when the computer device runs, the processor It communicates with the memory through a bus, and when the machine-readable instructions are executed by the processor, the above-mentioned first aspect or the steps in any possible implementation manner of the first aspect are performed.
- an embodiment of the present disclosure further provides a computer-readable storage medium, where a computer program stored on the computer program is executed by a processor to execute the steps in the first aspect or any possible implementation manner of the first aspect .
- FIG. 1 shows a flowchart of a point cloud data processing method provided by an embodiment of the present disclosure
- FIG. 2 shows a schematic diagram of coordinates of each position point of a cuboid provided by an embodiment of the present disclosure
- FIG. 3 shows a flowchart of a method for determining coordinate information of the reference position point provided by an embodiment of the present disclosure
- FIG. 4 shows a flowchart of a method for determining a detection result provided by an embodiment of the present disclosure
- 5A shows a schematic diagram of a grid matrix before encoding provided by an embodiment of the present disclosure
- FIG. 5B shows a schematic diagram of a sparse matrix provided by an embodiment of the present disclosure
- 5C shows a schematic diagram of an encoded grid matrix provided by an embodiment of the present disclosure
- FIG. 6A shows a schematic diagram of a left-shifted grid matrix provided by an embodiment of the present disclosure
- FIG. 6B shows a schematic diagram of a logical OR operation provided by an embodiment of the present disclosure
- FIG. 7A shows a schematic diagram of a grid matrix after a first inversion operation provided by an embodiment of the present disclosure
- FIG. 7B shows a schematic diagram of a grid matrix after a convolution operation provided by an embodiment of the present disclosure
- FIG. 8 shows a schematic diagram of the architecture of a point cloud data processing apparatus provided by an embodiment of the present disclosure
- FIG. 9 shows a schematic structural diagram of a computer device provided by an embodiment of the present disclosure.
- the present disclosure provides a point cloud data processing method and device, which can screen the point cloud data to be processed collected by the radar device in the target scene based on the effective perception range information corresponding to the target scene, and the screened target point Cloud data is the point cloud data that is valid in the target scene. Therefore, based on the filtered target point cloud data, the detection calculation is performed in the target scene, which can reduce the amount of calculation, improve the calculation efficiency, and the utilization of computing resources in the target scene. Rate.
- the execution subject of the point cloud data processing method provided by the embodiment of the present disclosure is generally a computer with a certain computing capability.
- equipment the computer equipment for example includes: terminal equipment or server or other processing equipment, the terminal equipment can be user equipment (User Equipment, UE), mobile equipment, user terminal, terminal, personal digital processing (Personal Digital Assistant, PDA), computing equipment, vehicle equipment, etc.
- the point cloud data processing method may be implemented by a processor calling computer-readable instructions stored in a memory.
- an embodiment of the present disclosure provides a point cloud data processing method, the method includes steps 101 to 103, wherein:
- Step 101 Obtain point cloud data to be processed obtained by scanning the radar device in the target scene.
- Step 102 Screen out target point cloud data from the to-be-processed point cloud data according to the effective perception range information corresponding to the target scene.
- Step 103 Detect the target point cloud data to obtain a detection result.
- the radar device can be deployed on an intelligent driving device, and during the driving process of the intelligent driving device, the radar device can scan to obtain point cloud data to be processed.
- the effective sensing range information may include coordinate thresholds on each coordinate dimension in a reference coordinate system, where the reference coordinate system is a three-dimensional coordinate system.
- the effective perception range information may be description information that constitutes a cuboid.
- the description information may be the coordinate thresholds of the length, width, and height of the cuboid in each coordinate dimension in the reference coordinate system, including the x-axis direction.
- FIG. 2 shows the structure based on the maximum value x_max and the minimum value x_min in the x-axis direction, the maximum value y_max and the minimum value y_min in the y-axis direction, and the maximum value z_max and the minimum value z_min in the z-axis direction.
- the coordinates of each position point of the cuboid, the coordinate origin can be the lower left vertex of the cuboid, and its coordinate value is (x_min, y_min, z_min).
- the effective sensing range information may also be description information of a sphere, a cube, etc. For example, only the radius of a sphere or the length, width, and height of a cube is given.
- the specific effective sensing range information can be based on The actual application scenario is described, and the present disclosure is not limited.
- the constraints on the effective sensing range can be preset.
- the values of x_max, y_max, and z_max can be set to be less than or equal to 200 meters.
- the calculation based on the point cloud data is based on the operation of the spatial voxels corresponding to the point cloud data, such as the layer-by-layer learning network (VoxelNet) based on the three-dimensional spatial information of the point cloud. Therefore, in this application scenario, in the case of limiting the coordinate thresholds of the reference radar scanning point in each coordinate dimension in the reference coordinate system, it is also possible to limit the number of spatial voxels of the reference radar scanning point in each coordinate dimension not to exceed the space volume pixel threshold.
- the spatial voxels corresponding to the point cloud data such as the layer-by-layer learning network (VoxelNet) based on the three-dimensional spatial information of the point cloud. Therefore, in this application scenario, in the case of limiting the coordinate thresholds of the reference radar scanning point in each coordinate dimension in the reference coordinate system, it is also possible to limit the number of spatial voxels of the reference radar scanning point in each coordinate dimension not to exceed the space volume pixel threshold.
- the number of spatial voxels in each coordinate dimension can be calculated by the following formula:
- N_x (x_max–x_min)/x_gridsize
- N_y (y_max–y_min)/y_gridsize
- N_z (z_max ⁇ z_min)/z_gridsize.
- x_gridsize, y_gridsize, z_gridsize respectively represent the preset resolutions corresponding to each dimension
- N_x represents the number of spatial voxels in the x-axis direction
- N_y represents the number of spatial voxels in the y-axis direction
- N_z represents the z-axis direction. The number of spatial voxels on .
- the calculation based on the point cloud data may also be an algorithm based on the point cloud data within the area of the top view, such as the point cloud-based fast target detection framework (PointPillars).
- PointPillars point cloud-based fast target detection framework
- Limit the top-view voxel area for example, you can limit the value of N_x*N_y.
- the effective sensing range information obtained in advance based on experiments may be obtained, and the effective sensing range information may be used as a preset and A fixed value, and the limited perceptual range information also obeys the above constraints.
- the computing resource information of the processing device may also be obtained first; The effective perception range information of .
- the computing resource information includes at least one of the following information: the memory of the central processing unit (CPU), the video memory of the graphics processing unit (GPU), and the computing resources of the field programmable logic gate array (FPGA).
- CPU central processing unit
- GPU graphics processing unit
- FPGA field programmable logic gate array
- the corresponding relationship between the computing resource information at each level and the effective sensing range information can be preset, and then when the method provided by the present disclosure is applied For different electronic devices, the effective sensing range information that matches the computing resource information of the electronic device can be searched based on the comparison relationship, or, when it is detected that the computing resource information of the electronic device changes, the effective sensing range can be dynamically adjusted. range information.
- the correspondence between the computing resource information of each level and the effective sensing range information may be obtained through an experimental test in advance.
- the effective coordinate range when selecting the target point cloud data from the point cloud data to be processed according to the effective sensing range information corresponding to the target scene, the effective coordinate range may be determined based on the effective sensing range information first, and then based on the effective sensing range information. Valid coordinate range, filter out the target point cloud data from the point cloud data to be processed.
- both the effective sensing range information and the effective coordinate range are fixed; the effective coordinate range can be changed according to the change of the effective sensing range information.
- the effective sensing range information may be the description information of the cuboid, including the length, width and height of the cuboid, and the radar device is used as the intersection of the body diagonals of the cuboid. If the position does not change, the cuboid is fixed, and the coordinate range in the cuboid is the valid coordinate range, so the valid coordinate range is also fixed.
- the position information of the reference position point within the effective sensing range and the coordinate information of the reference position point in the target scene may be used to determine the effective coordinate range. , and determine the effective coordinate range corresponding to the target scene.
- the effective sensing range information may be the description information of the cuboid, and the reference position point may be the intersection of the body diagonals of the cuboid. With the change of the reference position point, the effective sensing range information will also be available in different target scenarios. changes, so the corresponding valid coordinate range also changes.
- the coordinate information of the reference position point in the target scene may be the coordinate information of the reference position point in the radar coordinate system corresponding to the target scene, and the radar coordinate system may be used for collecting point cloud data in the target scene.
- the reference position point may be the intersection of the body diagonals of the cuboid. If the effective sensing range information is the description information of a sphere, the reference position point may be the center of the sphere. , or, the reference position point can be any reference radar scanning point within the effective sensing range information.
- the effective coordinate range corresponding to the target scene when determining the effective coordinate range corresponding to the target scene based on the position information of the reference position point within the effective perception range and the coordinate information of the reference position point in the target scene, the effective coordinate range corresponding to the target scene may be determined based on The coordinate information of the reference position point in the radar coordinate system, convert the coordinate thresholds in each coordinate dimension in the effective sensing range information in the reference coordinate system into the coordinates in each coordinate dimension in the radar coordinate system threshold.
- the reference position point may have corresponding first coordinate information in the reference coordinate system, and may have corresponding second coordinate information in the radar coordinate system.
- the coordinate thresholds of the reference radar scanning points in the effective sensing range information in each coordinate dimension under the reference coordinate system can be converted into the coordinate thresholds in the reference coordinate system. Coordinate thresholds in each coordinate dimension in the radar coordinate system.
- the relative positional relationship between the threshold coordinate point corresponding to the coordinate threshold of each coordinate dimension of the reference radar scanning point in the effective sensing range information in the reference coordinate system and the reference position point may be determined first. , and then, based on the relative positional relationship, determine the coordinate thresholds of the reference radar scanning points in the effective sensing range information in each coordinate dimension in the reference coordinate system and the coordinate thresholds in each coordinate dimension in the radar coordinate system.
- the coordinate thresholds of the reference radar scanning point in each coordinate dimension in the radar coordinate system in the effective sensing range information determined based on the coordinate information of the reference position point will also change accordingly. That is, the effective coordinate range corresponding to the target scene will also change, so it is possible to control the effective coordinate range in different target scenes by controlling the coordinate information of the reference position point.
- the radar scanning with the corresponding coordinate information located within the effective coordinate range may be performed. Points are used as radar scanning points in the target point cloud data.
- the three-dimensional coordinate information of the radar scan point can be stored, and then based on the three-dimensional coordinate information of the radar scan point, it can be determined whether the radar scan point is within the effective coordinate range.
- the three-dimensional coordinate information of the radar scanning point is (x, y, z)
- the three-dimensional coordinate information of the radar scanning point can be determined. Whether the coordinate information meets the following conditions:
- the application of the above point cloud data processing method will be introduced in combination with specific application scenarios.
- the above-mentioned point cloud data processing method can be applied to an automatic driving scene.
- the intelligent driving device is provided with a radar device.
- the coordinate information of the reference position point can be determined by the method as shown in FIG. 3 , and the method includes: The following steps 301 to 303.
- Step 301 Acquire location information of the intelligent driving device on which the radar device is set.
- the location information of the intelligent driving device for example, it can be acquired based on a Global Positioning System (Global Positioning System, GPS), and the present disclosure does not limit other ways in which the location information of the intelligent driving device can be acquired.
- GPS Global Positioning System
- Step 302 Determine the road type of the road where the smart driving device is located based on the location information of the smart driving device.
- the road type of each road within the drivable range of the intelligent driving device may be preset, and the road type may include, for example, an intersection, a T-junction, a highway, a parking lot, etc., based on the location information of the intelligent driving device
- the road on which the intelligent driving device is located may be determined, and then the road type of the road where the intelligent driving device is located may be determined according to the preset road type of each road within the drivable range of the intelligent driving device.
- Step 303 Acquire coordinate information of a reference position point matching the road type.
- the location of the point cloud data that needs to be focused on processing may be different for different road types. For example, if the intelligent driving device is located on a highway, the point cloud data that the intelligent driving device needs to process may be the point cloud data in front of the intelligent driving device. When the device is located at an intersection, the point cloud data that the intelligent driving device needs to process may be the point cloud data around the intelligent driving device. Screening of point cloud data under road type.
- the point cloud data that the intelligent driving device needs to process may be different when it is located on roads of different road types. Therefore, by obtaining the coordinate information of the reference position point matching the road type, the intelligent driving device can determine the current The valid coordinate range of the road type where it is located, so as to filter out the point cloud data under the corresponding road type, thereby improving the accuracy of screening point cloud data.
- the target point cloud data after the target point cloud data is screened out from the point cloud data to be processed, the target point cloud data can also be detected, and after the detection result is obtained, based on the detection result, the control settings Intelligent driving equipment for radar installations.
- the detection of the object to be recognized (for example, an obstacle) during the driving process of the intelligent driving device can be realized based on the filtered target point cloud data.
- Controlling the driving of the intelligent driving device may be controlling the acceleration, deceleration, steering, braking, and the like of the intelligent driving device.
- the detection result includes the position of the object to be identified in the target scene.
- the process of detecting the target point cloud data will be described in detail below with reference to specific embodiments, as shown in FIG. 4 .
- An embodiment of the present disclosure provides a method for determining a detection result, which includes the following steps:
- Step 401 Perform grid processing on the target point cloud data to obtain a grid matrix; the value of each element in the grid matrix is used to represent whether there is a target point at the corresponding grid.
- the point corresponding to the target point cloud data is called a target point.
- Step 402 Generate a sparse matrix corresponding to the to-be-identified object according to the grid matrix and the size information of the to-be-identified object in the target scene.
- Step 403 Determine the position of the object to be identified in the target scene based on the generated sparse matrix.
- rasterization may be performed first, and then the raster matrix obtained by the rasterization may be sparsely processed to generate a sparse matrix.
- the rasterization process here can be to map the spatially distributed target point cloud data including each target point into a set grid, and perform grid coding based on the target points corresponding to the grid (corresponding to a zero-one matrix ) process, the sparse processing process may be based on the size information of the object to be identified in the target scene to perform expansion processing operation on the above zero-one matrix (corresponding to the processing result of increasing the elements indicated as 1 in the zero-one matrix) or erosion processing The process of the operation (corresponding to the processing result of reducing the elements indicated as 1 in the zero-one matrix).
- the above-mentioned rasterization process and thinning process will be further described.
- the target points distributed in the Cartesian continuous real number coordinate system may be converted into the rasterized discrete coordinate system.
- the embodiment of the present disclosure has target points such as point A (0.32m, 0.48m), point B (0.6m, 0.4801m), and point C (2.1m, 3.2m), and rasterization is performed with 1m as the grid width,
- the range from (0m,0m) to (1m,1m) corresponds to the first grid
- the range from (0m,1m) to (1m,2m) corresponds to the second grid, and so on.
- the gridded A'(0,0) and B'(0,0) are in the grid of the first row and the first column, and C'(2,3) can be in the grid of the second row and the third column. Gerry, thus realizing the conversion from the Cartesian continuous real coordinate system to the discrete coordinate system.
- the coordinate information about the target point may be determined by the reference reference point (for example, the location of the radar device that collects the point cloud data), which will not be repeated here.
- two-dimensional rasterization can be performed, and three-dimensional rasterization can also be performed.
- the three-dimensional rasterization adds height information on the basis of the two-dimensional rasterization.
- the limited space can be divided into N*M grids, which are generally divided at equal intervals, and the interval size can be configured.
- a zero-one matrix ie, the above grid matrix
- Each grid can be represented by coordinates consisting of a unique row number and column number. and the above target point, the grid is encoded as 1, otherwise it is 0, so that the encoded zero-one matrix can be obtained.
- a sparse processing operation may be performed on the elements in the grid matrix according to the size information of the object to be identified in the target scene to generate a corresponding sparse matrix.
- the size information about the object to be recognized may be acquired in advance.
- the size information of the object to be recognized may be determined in combination with the image data synchronously collected from the target point cloud data, and the size information of the object to be recognized may also be roughly estimated based on specific application scenarios. Identify the size information of the object.
- the object in front of the vehicle can be a vehicle, and its general size information can be determined to be 4m ⁇ 4m.
- the embodiment of the present disclosure may also determine the size information of the object to be recognized based on other manners, which is not specifically limited in the embodiment of the present disclosure.
- the related sparse processing operation may be performing at least one expansion processing operation on the target element in the grid matrix (that is, the element representing the existence of the target point at the corresponding grid), and the expansion processing operation here may be performed on the grid matrix. It is performed when the coordinate range of the grid matrix is smaller than the size of the object to be recognized in the target scene, that is, through one or more expansion processing operations, the range of elements representing the existence of the target point at the corresponding grid can be performed step by step.
- the sparse processing operation in the embodiment of the present disclosure may also be performed on the target element in the grid matrix at least A corrosion processing operation, where the corrosion processing operation can be performed when the coordinate range of the grid matrix is larger than the size of the object to be identified in the target scene, that is, through one or more corrosion processing operations, the representation can be The range of elements in which the target point exists at the corresponding grid is gradually reduced, so that the reduced range of elements can be matched with the object to be identified, thereby realizing the determination of the position.
- one expansion processing operation which of the following operations is performed: one expansion processing operation, multiple expansion processing operations, one erosion processing operation, and multiple erosion processing operations, depending on the sparse matrix obtained by performing at least one shift processing and logic operation processing Whether the difference between the coordinate range of the target scene and the size of the object to be recognized in the target scene belongs to the preset threshold range, that is, the expansion or erosion processing operation adopted in the present disclosure is based on the constraint of the size information of the object to be recognized to make the information represented by the determined sparse matrix more consistent with the relevant information of the object to be identified.
- the purpose of the sparse processing whether based on the dilation processing operation or the erosion processing operation is to enable the generated sparse matrix to represent more accurate relevant information of the object to be identified.
- the above-mentioned dilation processing operation may be implemented based on a shift operation and a logical OR operation, or may be implemented based on convolution followed by negation, and negation after convolution.
- the two operations use different methods, but the final result of the resulting sparse matrix can be consistent.
- the above-mentioned erosion processing operation may be implemented based on a shift operation and a logical AND operation, or may be implemented directly based on a convolution operation.
- the two operations use different methods, the final result of the generated sparse matrix can be the same.
- 5A is a schematic diagram of a grid matrix obtained after grid processing (corresponding to before uncoding), by performing an eight-neighborhood analysis on each target element (corresponding to a grid with a filling effect) in the grid matrix once Dilation operation, that is, the corresponding sparse matrix 5B can be obtained. It can be seen that, for the target element with the target point at the corresponding grid in 5A, the embodiment of the present disclosure performs an eight-neighbor expansion operation, so that each target element becomes an element set after expansion, and the element The grid width corresponding to the set may match the size of the object to be identified.
- the expansion operation of the above-mentioned eight neighborhoods may be a process of determining an element whose absolute value of the difference between the abscissa or ordinate of the above-mentioned target element does not exceed 1. Except for the elements at the edge of the grid, generally all elements in the neighborhood of an element are There are eight elements (corresponding to the above element set), the input of the expansion processing result can be the coordinate information of the six target elements, and the output can be the coordinate information of the element set in the eight neighborhoods of the target element, as shown in FIG. 5B .
- a four-neighbor expansion operation can also be performed, and the latter and other expansion operations are not specifically limited herein.
- the embodiment of the present disclosure can also perform multiple dilation operations. For example, based on the dilation result shown in FIG. 5B , the dilation operation is performed again to obtain a sparse matrix with a larger range of element sets. No longer.
- the position of the object to be identified in the target scene can be determined.
- the embodiments of the present disclosure can be specifically implemented through the following two aspects.
- the position range of the object to be identified can be determined based on the correspondence between each element in the grid matrix and the coordinate range information of each target point. Specifically, the following steps can be used to achieve:
- Step 1 Based on the correspondence between each element in the grid matrix and the coordinate range information of each target point, determine the coordinate information of the target point corresponding to each target element in the generated sparse matrix;
- Step 2 Combine the coordinate information of the target points corresponding to each target element in the sparse matrix to determine the position of the object to be identified in the target scene.
- each target element in the grid matrix may correspond to multiple target points.
- the coordinate range information of the target points corresponding to the relevant elements and the multiple target points may be preset. definite.
- the coordinate information of the target point corresponding to each target element in the sparse matrix can be determined based on the predetermined correspondence between the above-mentioned elements and the coordinate range information of each target point. That is, the processing operation of de-rasterization is performed.
- the sparse matrix is obtained based on the sparse processing of the elements in the grid matrix that represent the target points at the corresponding grids
- the values of the target elements in the sparse matrix here can also represent the corresponding A target point exists at the grid.
- point A'(0,0) and point B'(0,0) indicated by the sparse matrix are in the first row and first column of the grid; point C'(2,3) is in the second row and third column
- point A'(0,0) and point B'(0,0) indicated by the sparse matrix are in the first row and first column of the grid; point C'(2,3) is in the second row and third column
- the grid as an example, in the process of de-rasterization, after the first grid (0,0) uses its center to map back to the Cartesian coordinate system, we can get (0.5m, 0.5m), the second row
- the grid (2,3) in the third column, using its center to map back to the Cartesian coordinate system can get (2.5m, 3.5m), that is, (0.5m, 0.5m) and (2.5m, 3.5m) It is determined as the mapped coordinate information, so that the position of the object to be identified in the target scene can be determined by combining the mapped coordinate information.
- the embodiments of the present disclosure can not only determine the location range of the object to be recognized based on the approximate relationship between the sparse matrix and the target detection result, but also determine the location range of the object to be recognized based on the trained convolutional neural network.
- the embodiments of the present disclosure may first perform at least one convolution process on the generated sparse matrix based on the trained convolutional neural network, and then determine the position range of the object to be recognized based on the convolution result obtained by the convolution process.
- the embodiments of the present disclosure can be implemented by combining shift processing and logical operations, and can also be implemented based on inversion followed by convolution, and convolution followed by inversion.
- one or more expansion processing operations may be performed based on at least one shift processing and logical OR operation.
- the size information of the object is determined.
- the target element representing the existence of the target point at the corresponding grid can be shifted in multiple preset directions to obtain a plurality of corresponding shifted grid matrices.
- the grid matrix and the plurality of shifted grid matrices corresponding to the first expansion processing operation are logically ORed, so that the sparse matrix after the first expansion processing operation can be obtained.
- it can be judged whether the coordinate range of the obtained sparse matrix is less than The size of the object to be identified, and whether the corresponding difference is large enough (for example, greater than a preset threshold), if so, the target element in the sparse matrix after the first expansion processing operation can be shifted in multiple preset directions according to the above method.
- Bit processing and logical OR operation to obtain the sparse matrix after the second expansion processing operation, and so on, until it is determined that the difference between the coordinate range of the newly obtained sparse matrix and the size of the object to be identified in the target scene belongs to the preset value.
- the threshold range is set, the sparse matrix is determined.
- the sparse matrix is essentially a zero-one matrix.
- the number of target elements in the obtained sparse matrix representing the existence of target points at the corresponding grid also increases, and since the grid mapped by the zero-one matrix has width information,
- the coordinate range corresponding to each target element in the sparse matrix can be used to verify whether the size of the object to be recognized in the target scene is reached, thereby improving the accuracy of subsequent target detection applications.
- Step 1 Select a shifted grid matrix from a plurality of shifted grid matrices
- Step 2 Perform a logical OR operation on the grid matrix before the current expansion processing operation and the selected shifted grid matrix to obtain an operation result;
- Step 3 Repeat the steps of selecting grid matrices that are not involved in the operation from the shifted grid matrices, and performing a logical OR operation on the selected grid matrix and the result of the latest operation, until all the grid matrices are selected.
- Grid matrix to get the sparse matrix after the current dilation operation.
- a shifted grid matrix can be selected from the shifted grid matrices.
- the grid matrix before the current expansion processing operation can be compared with the selected shifted grid matrix.
- the sparse matrix after the current expansion processing operation can be obtained.
- the expansion processing operation in this embodiment of the present disclosure may be a four-neighbor expansion operation centered on the target element, an eight-neighbor expansion operation centered on the target element, or other neighborhood processing operation methods.
- a corresponding neighborhood processing operation mode may be selected based on the size information of the object to be recognized, which is not specifically limited here.
- the corresponding preset directions of the shift processing are not the same.
- the grid matrix can be shifted according to the four preset directions.
- Bit processing which are left shift, right shift, up shift and down shift.
- the grid matrix can be shifted according to eight preset directions, respectively left shift, right shift. Move, move up, move down, move up and down under the premise of moving left, and move up and down under the premise of moving right.
- first perform a logical OR operation after determining the shifted grid matrix based on multiple shift directions, first perform a logical OR operation, and then perform multiple logical OR operations on the result. The shift operation in the shift direction is performed, and then the next logical OR operation is performed, and so on, until the dilated sparse matrix is obtained.
- the grid matrix before encoding shown in FIG. 5A can be converted into the grid matrix after encoding as shown in FIG. 5C , and then the first expansion processing operation is performed in conjunction with FIG. 6A to FIG. 6B .
- the grid matrix shown in FIG. 5C is taken as a zero-one matrix, the positions of all "1"s in the matrix can represent the grid where the target element is located, and all the "0"s in the matrix can represent the background.
- the matrix shift may be used to determine the neighborhood of all elements in the zero-one matrix whose element value is 1.
- the left shift means that the column coordinates corresponding to the elements with the element value of 1 in the zero-one matrix are subtracted by one, as shown in Figure 6A;
- the right-shift means that the column coordinates corresponding to all the elements in the zero-one matrix with the element value of 1 are added by one;
- Moving up means adding one to the row coordinates corresponding to all elements whose value is 1 in the zero-one matrix; moving down means adding one to the row coordinates corresponding to all elements in the zero-one matrix having a value of 1.
- embodiments of the present disclosure may combine the results of all neighborhoods using a matrix logical OR operation.
- Matrix logical OR operation that is, in the case of receiving two sets of zero-one matrix inputs with the same size, perform logical OR operation on the zero-one in the same position of the two sets of matrices in turn, and the obtained result forms a new zero-one matrix as the output,
- FIG. 6B A specific example of a logical OR operation is shown in FIG. 6B .
- the left-shifted grid matrix, the right-shifted grid matrix, the up-shifted grid matrix, and the down-shifted grid matrix can be sequentially selected to participate in the logical OR operation middle. For example, you can first perform a logical OR operation on the grid matrix with the grid matrix after shifting to the left, and the obtained operation result can perform a logical OR operation with the grid matrix after shifting right. The subsequent grid matrix is subjected to a logical OR operation, and the obtained operation result can be subjected to a logical OR operation with the grid matrix after the downshift, so as to obtain the sparse matrix after the first expansion processing operation.
- the above-mentioned selection order of the grid matrix after translation is only a specific example. In practical applications, it can also be selected in combination with other methods.
- the logical OR operation is performed after the paired down shift, and the logical OR operation is performed after the left shift and the right shift are paired.
- the two logical OR operations can be performed synchronously, which can save computing time.
- the expansion processing operation can be implemented by combining convolution and two inversion processing. Specifically, the following steps can be implemented:
- Step 1 Perform a first inversion operation on the elements in the grid matrix before the current expansion processing operation to obtain the grid matrix after the first inversion operation;
- Step 2 Perform at least one convolution operation on the grid matrix after the first inversion operation based on the first preset convolution check to obtain a grid matrix with a preset sparsity after at least one convolution operation; the preset sparsity Determined by the size information of the object to be recognized in the target scene;
- Step 3 Perform a second inversion operation on the elements in the grid matrix with the preset sparsity after at least one convolution operation to obtain a sparse matrix.
- the expansion processing operation can be realized by the operations of convolution followed by inversion and inversion after convolution, and the obtained sparse matrix can also represent the relevant information of the object to be recognized to a certain extent.
- the above convolution operation can be automatically combined with the convolutional neural network used in subsequent applications such as target detection, so the detection efficiency can be improved to a certain extent.
- the inversion operation may be implemented based on a convolution operation, or may be implemented based on other inversion operation modes.
- a convolution operation can be used to implement the specific implementation.
- the convolution operation can be performed on other elements except the target element in the grid matrix before the current expansion processing operation based on the second preset convolution check to obtain the first inversion element
- the second preset convolution can also be based on kernel, perform the convolution operation on the target element in the grid matrix before the current expansion processing operation, and obtain the second inversion element.
- the first inversion element can be determined.
- At least one convolution operation may be performed on the grid matrix after the first inversion operation by using the first preset convolution check, so as to obtain a grid matrix with a preset sparsity.
- the expansion processing operation can be used as a means of increasing the number of target elements in the grid matrix
- the above convolution operation can be regarded as a process of reducing the number of target elements in the grid matrix (corresponding to the erosion processing operation)
- the convolution operation in the embodiment of the present disclosure is performed on the grid matrix after the first inversion operation, using the inversion operation combined with the erosion processing operation, and then performing the inversion operation again is equivalent to the above expansion The equivalent operation of the processing operation.
- the grid matrix after the first inversion operation is subjected to a convolution operation with the first preset convolution kernel to obtain the grid matrix after the first convolution operation.
- the grid matrix after the first convolution operation and the first preset convolution kernel can be convolved again to obtain the grid matrix after the second convolution operation. lattice matrix, and so on, until a lattice matrix with a preset sparsity can be determined.
- the above sparsity may be determined by the proportion distribution of target elements and non-target elements in the grid matrix.
- the convolution operation in the embodiment of the present disclosure may be one time or multiple times.
- the specific operation process of the first convolution operation can be described, including the following steps:
- Step 1 For the first convolution operation, select each grid sub-matrix from the grid matrix after the first inversion operation according to the size of the first preset convolution kernel and the preset step size;
- Step 2 For each selected grid sub-matrix, perform a product operation on the grid sub-matrix and the weight matrix to obtain a first operation result, and perform an addition operation on the first operation result and the offset to obtain a second operation result. operation result;
- Step 3 Determine the grid matrix after the first convolution operation based on the second operation result corresponding to each grid sub-matrix.
- the grid matrix after the first inversion operation can be traversed in a traversal manner, so that for each grid sub-matrix traversed, the grid sub-matrix and the weight matrix can be multiplied to obtain the first operation result, and add the first operation result and the offset to obtain the second operation result.
- the second operation result corresponding to each grid sub-matrix is combined into the corresponding matrix elements, and the first operation result can be obtained.
- the grid matrix after the convolution operation can be traversed in a traversal manner, so that for each grid sub-matrix traversed, the grid sub-matrix and the weight matrix can be multiplied to obtain the first operation result, and add the first operation result and the offset to obtain the second operation result.
- the encoded grid matrix shown in FIG. 5C is still taken as an example here, and the expansion processing operation is illustrated in conjunction with FIGS. 7A to 7B .
- a 1*1 convolution kernel (that is, a second preset convolution kernel) can be used to implement the first inversion operation.
- the weight of the second preset convolution kernel is -1 and the offset is 1.
- a 3*3 convolution kernel ie, the first preset convolution kernel
- a linear rectification function Rectified Linear Unit, ReLU
- Each weight value included in the above-mentioned first preset convolution kernel weight value matrix is 1, and the offset is 8.
- the formula ⁇ output ReLU(input grid matrix after the first inversion operation* weight + bias) ⁇ to achieve the above-mentioned corrosion processing operation.
- each nested layer of the convolutional network with the second preset convolution kernel can superimpose an erosion operation, so that a grid matrix with a fixed sparsity can be obtained, and the inversion operation again can be equivalent to an expansion processing operation. Thereby, the generation of sparse matrix can be realized.
- the embodiments of the present disclosure may be implemented in combination with shift processing and logical operations, and may also be implemented based on convolution operations.
- one or more corrosion processing operations can be performed based on at least one shift processing and logical AND operation.
- the specific number of corrosion processing operations can be combined with the target scene to be identified.
- the size information of the object is determined.
- the grid matrix shift processing can also be performed first.
- the difference from the above expansion processing is that here
- the logical operation of which can be a logical AND operation on the shifted grid matrix.
- the corrosion processing operation in the embodiment of the present disclosure may be four-neighbor corrosion centered on the target element, eight-area corrosion centered on the target element, or other field processing operations.
- the corresponding domain processing operation mode can be selected based on the size information of the object to be recognized, which is not specifically limited here.
- the erosion processing operation can be implemented in combination with the convolution processing, which can be specifically implemented by the following steps:
- Step 1 Perform at least one convolution operation on the grid matrix based on the third preset convolution check to obtain a grid matrix with a preset sparsity after at least one convolution operation; the preset sparsity is determined by the target scene to be identified. The size information of the object is determined;
- Step 2 Determine the grid matrix with the preset sparsity after at least one convolution operation as the sparse matrix corresponding to the object to be recognized.
- the above convolution operation can be regarded as a process of reducing the number of target elements in the grid matrix, that is, an erosion process.
- the grid matrix and the first preset convolution kernel are subjected to convolution operation to obtain the grid matrix after the first convolution operation, and the sparsity of the grid matrix after the first convolution operation is judged.
- the grid matrix after the first convolution operation and the third preset convolution kernel can be convolved again to obtain the grid matrix after the second convolution operation, and so on.
- a grid matrix with a preset sparsity can be determined, that is, a sparse matrix corresponding to the object to be recognized is obtained.
- the convolution operation in this embodiment of the present disclosure may be performed once or multiple times.
- the specific process of the convolution operation please refer to the relevant description of implementing expansion processing based on convolution and inversion in the first aspect above, which will not be repeated here.
- convolutional neural networks with different data processing bit widths can be used to generate sparse matrices.
- 4 bits can be used to represent the input, output, and computational parameters of the network Parameters, such as the element value (0 or 1) of the grid matrix, weights, offsets, etc., in addition, can also be represented by 8bit to adapt to the network processing bit width and improve the operation efficiency.
- the point cloud data to be processed collected by the radar device in the target scene can be screened based on the effective perception range information corresponding to the target scene, and the screened target point cloud data is the corresponding valid point cloud data in the target scene. Therefore, based on the filtered target point cloud data, the detection calculation is performed in the target scene, which can reduce the amount of calculation, improve the calculation efficiency, and the utilization rate of computing resources in the target scene.
- the writing order of each step does not mean a strict execution order but constitutes any limitation on the implementation process, and the specific execution order of each step should be based on its function and possible Internal logic is determined.
- the embodiment of the present disclosure also provides a point cloud data processing device corresponding to the point cloud data processing method. Similar, therefore, the implementation of the apparatus may refer to the implementation of the method, and repeated descriptions will not be repeated.
- the device includes: an acquisition module 801 , a screening module 802 , and a detection module 803 ; wherein,
- an acquisition module 801, configured to acquire point cloud data to be processed obtained by scanning the radar device in the target scene;
- a screening module 802 configured to screen out target point cloud data from the to-be-processed point cloud data according to the effective perception range information corresponding to the target scene;
- the detection module 803 is configured to detect the target point cloud data to obtain a detection result.
- the screening module 802 is further configured to determine the effective perception range information corresponding to the target scene according to the following manner:
- the effective sensing range information matched with the computing resource information is determined.
- the screening module 802 when screening out target point cloud data from the to-be-processed point cloud data according to the effective perception range information corresponding to the target scene, is used for:
- target point cloud data is filtered out from the point cloud data to be processed.
- the screening module 802 when determining the effective coordinate range based on the effective sensing range information, is used to:
- the effective coordinate range corresponding to the target scene is determined.
- the screening module 802 when screening out target point cloud data from the to-be-processed point cloud data based on the valid coordinate range, is used to:
- the radar scanning points whose corresponding coordinate information is located within the effective coordinate range are used as the radar scanning points in the target point cloud data.
- the screening module 802 is further configured to determine the coordinate information of the reference position point in the target scene according to the following manner:
- the coordinate information of the reference position point matching the road type is obtained.
- the detection result includes the position of the object to be identified in the target scene
- the detection module 803 when detecting the target point cloud data and obtaining a detection result, is used for:
- the position of the object to be identified in the target scene is determined.
- the detection module 803 is used for generating a sparse matrix corresponding to the object to be identified according to the grid matrix and the size information of the object to be identified in the target scene. :
- At least one expansion processing operation or erosion processing operation is performed on the target element in the grid matrix to generate a corresponding object to be identified.
- the value of the target element indicates that the target point exists at the corresponding grid.
- the detection module 803 when performing the expansion processing operation or the erosion processing operation, is used for: shift processing and logical operation processing, and the coordinate range of the sparse matrix is the same as that of the object to be identified.
- the difference between the sizes is within a preset threshold.
- the detection module 803 performs at least one expansion processing operation on the elements in the grid matrix according to the grid matrix and the size information of the object to be identified in the target scene. , when generating a sparse matrix corresponding to the object to be identified, used for:
- a second inversion operation is performed on the elements in the grid matrix with the preset sparsity after the at least one convolution operation to obtain the sparse matrix.
- the detection module 803 performs the first inversion operation on the elements in the grid matrix before the current expansion processing operation to obtain the grid matrix after the first inversion operation, using At:
- a convolution operation is performed on other elements except the target element in the grid matrix before the current expansion processing operation to obtain the first inversion element, and based on the second preset convolution kernel, perform the convolution operation on the target element in the grid matrix before the current expansion processing operation to obtain the second inversion element;
- the detection module 803 performs at least one convolution operation on the grid matrix after the first inversion operation based on the first preset convolution check, to obtain at least one convolution operation.
- a raster matrix with a preset sparsity is used:
- For the first convolution operation performing a convolution operation on the grid matrix after the first inversion operation and the first preset convolution kernel to obtain the grid matrix after the first convolution operation;
- the detection module 803 has a weight matrix and an offset corresponding to the weight matrix in the first preset convolution kernel; for the first convolution operation, the first The grid matrix after the reverse operation is subjected to a convolution operation with the first preset convolution kernel, and when the grid matrix after the first convolution operation is obtained, it is used for:
- each grid sub-matrix is selected from the grid matrix after the first inversion operation
- For each selected grid sub-matrix perform a product operation on the grid sub-matrix and the weight matrix to obtain a first operation result, and add the first operation result and the offset operation to obtain the second operation result;
- the grid matrix after the first convolution operation is determined.
- the detection module 803 performs at least one corrosion processing operation on the elements in the grid matrix according to the grid matrix and the size information of the object to be identified in the target scene. , when generating a sparse matrix corresponding to the object to be identified, used for:
- the grid matrix with the preset sparsity after the at least one convolution operation is determined as the sparse matrix corresponding to the object to be identified.
- the detection module 803 when performing grid processing on the target point cloud data to obtain a grid matrix, is used for:
- the detection module 803 when determining the position range of the object to be identified in the target scene based on the generated sparse matrix, is used for:
- the coordinate information of the target points corresponding to each of the target elements in the sparse matrix is combined to determine the position of the object to be identified in the target scene.
- the detection module 803 when determining the position of the object to be identified in the target scene based on the generated sparse matrix, is configured to:
- the position of the object to be identified in the target scene is determined.
- the device further includes a control module 804, configured to: after detecting the target point cloud data and obtaining a detection result, control and set the intelligent driving of the radar device based on the detection result. equipment.
- the point cloud data to be processed collected by the radar device in the target scene can be screened based on the effective perception range information corresponding to the target scene, and the screened target point cloud data is the target point cloud data corresponding to the target scene Therefore, based on the filtered point cloud data, the detection calculation is performed in the target scene, which can reduce the amount of calculation, improve the calculation efficiency, and the utilization rate of computing resources in the target scene.
- an embodiment of the present disclosure further provides a computer device, including a processor 901 , a memory 902 and a bus 903 .
- the memory 902 includes a memory 9021 and an external memory 9022 for storing execution instructions; the memory 9021 here is also called an internal memory, and is used for temporarily storing the operation data in the processor 901 and the data exchanged with the external memory 9022 such as a hard disk.
- the processor 901 exchanges data with the external memory 9022 through the memory 9021, and when the computer device 900 is running, the processor 901 and the memory 902 communicate through the bus 903, so that the processor 901 executes the following instructions:
- Embodiments of the present disclosure further provide a computer-readable storage medium, where a computer program stored on the computer program is executed by a processor to execute the point cloud data processing method described in the foregoing method embodiments.
- the storage medium may be a volatile or non-volatile computer-readable storage medium.
- the computer program product of the method for processing point cloud data provided by the embodiments of the present disclosure includes a computer-readable storage medium storing program codes, and the instructions included in the program codes can be used to execute the point cloud data described in the above method embodiments.
- the processing method reference may be made to the foregoing method embodiments, and details are not described herein again.
- Embodiments of the present disclosure also provide a computer program, which implements any one of the methods in the foregoing embodiments when the computer program is executed by a processor.
- the computer program product can be specifically implemented by hardware, software or a combination thereof.
- the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), etc. Wait.
- the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
- each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
- the functions, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-executable non-volatile computer-readable storage medium.
- the computer software products are stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present disclosure.
- the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes .
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Abstract
Description
Claims (20)
- 一种点云数据处理方法,包括:A point cloud data processing method, comprising:获取雷达装置在目标场景下扫描得到的待处理点云数据;Obtain the point cloud data to be processed obtained by scanning the radar device in the target scene;根据所述目标场景对应的有效感知范围信息,从所述待处理点云数据中筛选出目标点云数据;Screening out target point cloud data from the to-be-processed point cloud data according to the effective perception range information corresponding to the target scene;对所述目标点云数据进行检测,得到检测结果。Detecting the target point cloud data to obtain a detection result.
- 根据权利要求1所述的方法,其特征在于,根据以下方式确定所述目标场景对应的所述有效感知范围信息:The method according to claim 1, wherein the effective perception range information corresponding to the target scene is determined according to the following manner:获取在所述目标场景下处理所述待处理点云数据的处理设备的计算资源信息;acquiring computing resource information of a processing device that processes the point cloud data to be processed in the target scene;基于所述计算资源信息,确定与所述计算资源信息所匹配的所述有效感知范围信息。Based on the computing resource information, the effective sensing range information matched with the computing resource information is determined.
- 根据权利要求1或2所述的方法,其特征在于,所述根据所述目标场景对应的有效感知范围信息,从所述待处理点云数据中筛选出目标点云数据,包括:The method according to claim 1 or 2, wherein the filtering out the target point cloud data from the to-be-processed point cloud data according to the effective perception range information corresponding to the target scene comprises:基于所述有效感知范围信息,确定有效坐标范围;determining an effective coordinate range based on the effective sensing range information;基于所述有效坐标范围,从所述待处理点云数据中筛选出目标点云数据。Based on the valid coordinate range, target point cloud data is filtered out from the point cloud data to be processed.
- 根据权利要求3所述的方法,其特征在于,所述基于所述有效感知范围信息,确定有效坐标范围,包括:The method according to claim 3, wherein the determining an effective coordinate range based on the effective sensing range information comprises:基于参考位置点在所述有效感知范围内的位置信息、以及所述参考位置点在所述目标场景中的坐标信息,确定所述目标场景对应的有效坐标范围。Based on the position information of the reference position point within the effective perception range and the coordinate information of the reference position point in the target scene, the effective coordinate range corresponding to the target scene is determined.
- 根据权利要求3所述的方法,其特征在于,所述基于所述有效坐标范围,从所述待处理点云数据中筛选出目标点云数据,包括:The method according to claim 3, wherein the filtering out target point cloud data from the to-be-processed point cloud data based on the valid coordinate range comprises:将所述待处理点云数据中的坐标信息位于所述有效坐标范围内的每一个雷达扫描点作为所述目标点云数据中的雷达扫描点。Each radar scan point whose coordinate information in the point cloud data to be processed is located within the effective coordinate range is taken as a radar scan point in the target point cloud data.
- 根据权利要求4所述的方法,其特征在于,根据以下方式确定所述参考位置点在所述目标场景中的坐标信息:The method according to claim 4, wherein the coordinate information of the reference position point in the target scene is determined according to the following manner:获取设置所述雷达装置的智能行驶设备的位置信息;obtaining the location information of the intelligent driving equipment in which the radar device is set;基于所述智能行驶设备的位置信息确定所述智能行驶设备所在道路的道路类型;Determine the road type of the road where the intelligent driving device is located based on the location information of the intelligent driving device;获取与所述道路类型相匹配的参考位置点的坐标信息作为所述参考位置点在所述目标场景中的坐标信息。The coordinate information of the reference position point matching the road type is acquired as the coordinate information of the reference position point in the target scene.
- 根据权利要求1所述的方法,其特征在于,所述检测结果包括在所述目标场景中待识别对象的位置;所述对所述目标点云数据进行检测,得到检测结果,包括:The method according to claim 1, wherein the detection result includes the position of the object to be identified in the target scene; and the detection of the target point cloud data to obtain the detection result includes:对所述目标点云数据进行栅格化处理,得到栅格矩阵;所述栅格矩阵中每个元素的值用于表征对应的栅格处是否存在目标点;Perform grid processing on the target point cloud data to obtain a grid matrix; the value of each element in the grid matrix is used to represent whether there is a target point at the corresponding grid;根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,生成与所述待识别对象对应的稀疏矩阵;generating a sparse matrix corresponding to the object to be identified according to the grid matrix and the size information of the object to be identified in the target scene;基于生成的所述稀疏矩阵,确定所述待识别对象在所述目标场景中的位置。Based on the generated sparse matrix, the position of the object to be identified in the target scene is determined.
- 根据权利要求7所述的方法,其特征在于,所述根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,生成与所述待识别对象对应的稀疏矩阵,包括:The method according to claim 7, wherein generating a sparse matrix corresponding to the to-be-identified object according to the grid matrix and the size information of the to-be-identified object in the target scene comprises:根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,对所述栅格矩阵中的目标元素进行至少一次膨胀处理操作或者腐蚀处理操作,生成与所述待识别对象对应的稀疏矩阵;According to the grid matrix and the size information of the object to be identified in the target scene, at least one expansion processing operation or erosion processing operation is performed on the target element in the grid matrix to generate a corresponding object to be identified. sparse matrix;其中,所述目标元素的值表征对应的栅格处存在所述目标点。Wherein, the value of the target element indicates that the target point exists at the corresponding grid.
- 根据权利要求8所述的方法,其特征在于,所述膨胀处理操作或者腐蚀处理操作包括:The method according to claim 8, wherein the expansion treatment operation or the corrosion treatment operation comprises:移位处理以及逻辑运算处理,Shift processing and logical operation processing,所述稀疏矩阵的坐标范围与所述待识别对象的尺寸之间的差值在预设阈值范围内。The difference between the coordinate range of the sparse matrix and the size of the object to be identified is within a preset threshold range.
- 根据权利要求8所述的方法,其特征在于,根据所述栅格矩阵以及所述目标场 景中的待识别对象的尺寸信息,对所述栅格矩阵中的元素进行至少一次膨胀处理操作,生成与所述待识别对象对应的稀疏矩阵,包括:The method according to claim 8, wherein, according to the grid matrix and the size information of the object to be identified in the target scene, at least one expansion processing operation is performed on the elements in the grid matrix to generate The sparse matrix corresponding to the object to be identified, including:对当前次膨胀处理操作前的栅格矩阵中的元素进行第一取反操作,得到第一取反操作后的栅格矩阵;Perform a first inversion operation on the elements in the grid matrix before the current expansion processing operation to obtain the grid matrix after the first inversion operation;基于第一预设卷积核对所述第一取反操作后的栅格矩阵进行至少一次卷积运算,得到至少一次卷积运算后的具有预设稀疏度的栅格矩阵;Perform at least one convolution operation on the grid matrix after the first inversion operation based on the first preset convolution check to obtain a grid matrix with a preset sparsity after at least one convolution operation;对所述至少一次卷积运算后的具有预设稀疏度的栅格矩阵中的元素进行第二取反操作,得到所述稀疏矩阵。A second inversion operation is performed on the elements in the grid matrix with the preset sparsity after the at least one convolution operation to obtain the sparse matrix.
- 根据权利要求10所述的方法,其特征在于,所述对当前次膨胀处理操作前的栅格矩阵中的元素进行第一取反操作,得到第一取反操作后的栅格矩阵,包括:The method according to claim 10, wherein the performing a first inversion operation on the elements in the grid matrix before the current expansion processing operation to obtain the grid matrix after the first inversion operation, comprising:基于第二预设卷积核,对当前次膨胀处理操作前的栅格矩阵中除所述目标元素外的其它元素进行卷积运算,得到第一取反元素;Based on the second preset convolution kernel, perform a convolution operation on other elements except the target element in the grid matrix before the current expansion processing operation to obtain the first inversion element;基于所述第二预设卷积核,对当前次膨胀处理操作前的栅格矩阵中的目标元素进行卷积运算,得到第二取反元素;Based on the second preset convolution kernel, a convolution operation is performed on the target element in the grid matrix before the current expansion processing operation to obtain a second inversion element;基于所述第一取反元素和所述第二取反元素,得到第一取反操作后的栅格矩阵。Based on the first inversion element and the second inversion element, a grid matrix after the first inversion operation is obtained.
- 根据权利要求10或11所述的方法,其特征在于,所述基于第一预设卷积核对所述第一取反操作后的栅格矩阵进行至少一次卷积运算,得到至少一次卷积运算后的具有预设稀疏度的栅格矩阵,包括:The method according to claim 10 or 11, wherein at least one convolution operation is performed on the grid matrix after the first inversion operation based on the first preset convolution check to obtain at least one convolution operation The resulting raster matrix with preset sparsity, including:针对首次卷积运算,将所述第一取反操作后的栅格矩阵与所述第一预设卷积核进行卷积运算,得到首次卷积运算后的栅格矩阵;For the first convolution operation, performing a convolution operation on the grid matrix after the first inversion operation and the first preset convolution kernel to obtain the grid matrix after the first convolution operation;重复执行将上一次卷积运算后的栅格矩阵与所述第一预设卷积核进行卷积运算,得到当前次卷积运算后的栅格矩阵的步骤,直至得到具有所述预设稀疏度的栅格矩阵。Repeat the steps of performing the convolution operation on the grid matrix after the previous convolution operation with the first preset convolution kernel to obtain the grid matrix after the current convolution operation, until obtaining the grid matrix with the preset sparseness A raster matrix of degrees.
- 根据权利要求12所述的方法,其特征在于,所述第一预设卷积核具有权值矩阵以及与该权值矩阵对应的偏置量;所述针对首次卷积运算,将所述第一取反操作后的栅格矩阵与所述第一预设卷积核进行卷积运算,得到首次卷积运算后的栅格矩阵,包括:The method according to claim 12, wherein the first preset convolution kernel has a weight matrix and an offset corresponding to the weight matrix; for the first convolution operation, the first convolution kernel is Perform a convolution operation on the grid matrix after the inversion operation and the first preset convolution kernel to obtain the grid matrix after the first convolution operation, including:针对首次卷积运算,按照第一预设卷积核的尺寸以及预设步长,从所述第一取反操作后的栅格矩阵中选取每个栅格子矩阵;For the first convolution operation, according to the size of the first preset convolution kernel and the preset step size, each grid sub-matrix is selected from the grid matrix after the first inversion operation;针对选取的每个所述栅格子矩阵,将该栅格子矩阵与所述权值矩阵进行乘积运算,得到第一运算结果;For each selected grid sub-matrix, perform a product operation on the grid sub-matrix and the weight matrix to obtain a first operation result;将所述第一运算结果与所述偏置量进行加法运算,得到第二运算结果;performing an addition operation on the first operation result and the offset to obtain a second operation result;基于各个所述栅格子矩阵对应的第二运算结果,确定首次卷积运算后的栅格矩阵。Based on the second operation result corresponding to each of the grid sub-matrixes, the grid matrix after the first convolution operation is determined.
- 根据权利要求8所述的方法,其特征在于,根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,对所述栅格矩阵中的元素进行至少一次腐蚀处理操作,生成与所述待识别对象对应的稀疏矩阵,包括:The method according to claim 8, characterized in that, according to the grid matrix and the size information of the object to be identified in the target scene, performing at least one erosion processing operation on the elements in the grid matrix to generate The sparse matrix corresponding to the object to be identified, including:基于第三预设卷积核对待处理的栅格矩阵进行至少一次卷积运算,得到至少一次卷积运算后的具有预设稀疏度的栅格矩阵;Perform at least one convolution operation on the grid matrix to be processed based on the third preset convolution kernel to obtain a grid matrix with a preset sparsity after at least one convolution operation;将所述至少一次卷积运算后的具有预设稀疏度的栅格矩阵,确定为与所述待识别对象对应的稀疏矩阵。The grid matrix with the preset sparsity after the at least one convolution operation is determined as the sparse matrix corresponding to the object to be identified.
- 根据权利要求7至14任一所述的方法,其特征在于,The method according to any one of claims 7 to 14, wherein,所述对所述目标点云数据进行栅格化处理,得到栅格矩阵,包括:The rasterization process is performed on the target point cloud data to obtain a raster matrix, including:对所述目标点云数据进行栅格化处理,得到栅格矩阵以及该栅格矩阵中各个元素与各个目标点坐标范围信息之间的对应关系;Perform grid processing on the target point cloud data to obtain a grid matrix and the corresponding relationship between each element in the grid matrix and the coordinate range information of each target point;所述基于生成的所述稀疏矩阵,确定所述待识别对象在所述目标场景中的位置范围,包括:The determining the position range of the object to be identified in the target scene based on the generated sparse matrix includes:基于所述栅格矩阵中各个元素与各个目标点坐标范围信息之间的对应关系,确定生成的所述稀疏矩阵中每个目标元素所对应的目标点的坐标信息;Based on the correspondence between each element in the grid matrix and the coordinate range information of each target point, determine the coordinate information of the target point corresponding to each target element in the generated sparse matrix;将所述稀疏矩阵中各个所述目标元素所对应的目标点的坐标信息进行组合,确定所述待识别对象在所述目标场景中的位置。The coordinate information of the target points corresponding to each of the target elements in the sparse matrix is combined to determine the position of the object to be identified in the target scene.
- 根据权利要求7至15任一所述的方法,其特征在于,所述基于生成的所述稀疏矩阵,确定所述待识别对象在所述目标场景中的位置,包括:The method according to any one of claims 7 to 15, wherein the determining the position of the object to be identified in the target scene based on the generated sparse matrix comprises:基于已训练的卷积神经网络对生成的所述稀疏矩阵中的每个目标元素进行至少一次卷积处理,得到卷积结果;Perform at least one convolution process on each target element in the generated sparse matrix based on the trained convolutional neural network to obtain a convolution result;基于所述卷积结果,确定所述待识别对象在所述目标场景中的位置。Based on the convolution result, the position of the object to be identified in the target scene is determined.
- 根据权利要求1至16任一所述的方法,其特征在于,在对所述目标点云数据进行检测,得到检测结果之后,所述方法还包括:The method according to any one of claims 1 to 16, wherein after detecting the target point cloud data and obtaining a detection result, the method further comprises:基于所述检测结果控制设置有所述雷达装置的智能行驶设备。The intelligent traveling equipment provided with the radar device is controlled based on the detection result.
- 一种点云数据处理装置,包括:A point cloud data processing device, comprising:获取模块,用于获取雷达装置在目标场景下扫描得到的待处理点云数据;The acquisition module is used to acquire the point cloud data to be processed obtained by scanning the radar device in the target scene;筛选模块,用于根据所述目标场景对应的有效感知范围信息,从所述待处理点云数据中筛选出目标点云数据;a screening module, configured to screen out target point cloud data from the to-be-processed point cloud data according to the effective perception range information corresponding to the target scene;检测模块,用于对所述目标点云数据进行检测,得到检测结果。The detection module is used for detecting the target point cloud data to obtain a detection result.
- 一种计算机设备,包括处理器、存储器和总线,所述存储器存储有所述处理器可执行的机器可读指令,当计算机设备运行时,所述处理器与所述存储器之间通过总线通信,所述机器可读指令被所述处理器执行时执行如权利要求1至17任一所述的点云数据处理方法。A computer device includes a processor, a memory, and a bus, the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate through the bus, When the machine-readable instructions are executed by the processor, the method for processing point cloud data according to any one of claims 1 to 17 is performed.
- 一种计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器运行时执行如权利要求1至17任意一项所述的点云数据处理方法。A computer-readable storage medium having a computer program stored thereon, the computer program executing the point cloud data processing method according to any one of claims 1 to 17 when the computer program is run by a processor.
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