Adaptive grid division method based on 4D imaging millimeter wave radar point cloud

Abstract

The invention relates to the technical field of radar data processing, and particularly discloses a self-adaptive grid division method based on 4D imaging millimeter wave radar point clouds, which comprises the following steps: step S11, acquiring multiple pieces of dimension information of target point clouds around a vehicle; s12, establishing a grid map according to the current dimension information of the target point cloud around the vehicle; s13, distributing the target point clouds around the vehicle to the corresponding minimum grids on the grid map according to the current dimension information of the target point clouds, and calculating the density of the target point clouds in each minimum grid; step S14, according to the target point cloud density in each minimum grid, merging the plurality of minimum grids in the grid map to form a plurality of merged grids; and the step S12 to the step S14 are repeated until all the grid maps established by all the dimension information of the target point cloud around the vehicle are subjected to grid combination. According to the invention, through adaptive grid division, redundant grids are reduced, and a basis is provided for a subsequent rasterization clustering algorithm.

Description

Technical Field

[0001] The present invention relates to the field of radar data processing technology, and more specifically, to an adaptive grid division method based on 4D imaging millimeter wave radar point cloud. Background Art

[0002] The point cloud resolution of traditional 3D millimeter wave radar is low and it does not have the ability to measure height. The mainstream rasterization algorithm can quickly process the radar point cloud to obtain clustering results. The rasterization algorithm converts the point cloud into a standardized grid, avoiding the impact of the number of points on the clustering time, and has a better processing effect on regional dense point clouds.

[0003] The point cloud resolution of 4D imaging millimeter wave radar has been greatly improved, and it has the ability to measure height, and the point cloud has increased in magnitude. Therefore, the mainstream rasterization algorithm is not effective. The fixed step size rasterization method has the following defects:

[0004] Low computational efficiency: When processing dense and non-uniform data areas, the fixed-step rasterization method tends to generate a large number of redundant grids. These redundant grids cannot provide effective clustering information, but increase the computational burden and reduce processing efficiency.

[0005] Poor clustering effect: In sparse data areas, fixed-step grid division cannot fully extract the structural features of the point cloud. The accuracy is limited by the step size, resulting in poor clustering effect and difficulty in accurately identifying the edges of target objects. Summary of the invention

[0006] The purpose of the present invention is to overcome the deficiencies in the prior art and provide an adaptive grid division method based on 4D imaging millimeter wave radar point cloud to solve the problems of low computational efficiency and poor clustering effect in the existing fixed step size rasterization method.

[0007] As a first aspect of the present invention, an adaptive grid division method based on 4D imaging millimeter wave radar point cloud is provided, and the adaptive grid division method based on 4D imaging millimeter wave radar point cloud comprises the following steps:

[0008] Step S11: acquiring multiple dimensional information of the target point cloud around the vehicle collected by the 4D imaging millimeter wave radar;

[0009] Step S12: establishing a grid map according to the current dimension information of the target point cloud around the vehicle, wherein the grid map includes a plurality of minimum grids whose grid sides are all of minimum step lengths;

[0010] Step S13: allocating the target point clouds around the vehicle to the corresponding minimum grids on the grid map according to their respective current dimension information, and calculating the density of the target point clouds in each minimum grid;

[0011] Step S14: According to the density of the target point cloud in each minimum grid, multiple minimum grids in the grid map are merged to form multiple merged grids; steps S12 to S14 are repeated until all grid maps established by all dimensional information of the target point cloud around the vehicle have completed grid merging.

[0012] Furthermore, the method of merging a plurality of minimum grids in the grid map according to the target point cloud density in each minimum grid to form a plurality of merged grids further includes:

[0013] For each minimum grid in the grid map, a density difference ratio between the target point cloud density in the current minimum grid and the target point cloud density in the adjacent minimum grid is calculated;

[0014] The two smallest rasters whose density difference ratio is less than the density difference threshold are merged to form a merged raster.

[0015] Furthermore, the step of calculating the density difference ratio between the target point cloud density in the current minimum grid and the target point cloud density in the adjacent minimum grid according to the target point cloud density in the current minimum grid also includes:

[0016] Density difference ratio ρ' n The calculation formula is as follows:

[0017]

[0018] Among them, ρ n Refers to the target point cloud density in the current minimum grid, ρ n-1 Refers to the target point cloud density in the adjacent minimum grid of the current minimum grid.

[0019] Furthermore, the value of the density difference threshold D_rate is 0.25.

[0020] The adaptive grid division method based on 4D imaging millimeter wave radar point cloud provided by the present invention has the following advantages:

[0021] (1) Compared with the traditional 3D millimeter-wave radar, the 4D imaging millimeter-wave radar based on the present invention has significantly improved point cloud resolution, and has height measurement capability, and greatly improved the ability to describe the target;

[0022] (2) The present invention is an improvement on the rasterization clustering of 4D imaging millimeter wave radar point cloud. By adaptively dividing the grids, reducing redundant grids, optimizing the algorithm time consumption, and improving the algorithm performance, the present invention solves the problem of long time consumption in the implementation of the rasterization clustering algorithm in 4D imaging millimeter wave radar, ensures the real-time performance of the algorithm, and also takes into account the clustering accuracy of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention, but do not constitute a limitation of the present invention.

[0024] Figure 1 A flowchart of the adaptive grid division method based on 4D imaging millimeter wave radar point cloud provided by the present invention.

[0025] Figure 2A-2B A schematic diagram comparing the original fixed-step grid division method provided by the present invention and the adaptive grid division method provided by the present invention. DETAILED DESCRIPTION

[0026] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of an adaptive grid division method based on 4D imaging millimeter wave radar point cloud proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In this embodiment, an adaptive grid division method based on 4D imaging millimeter wave radar point cloud is provided. Figure 1 As shown, the adaptive grid division method based on 4D imaging millimeter wave radar point cloud includes the following steps:

[0028] Step S11: Acquire multiple dimensional information of the target point cloud around the vehicle collected by the 4D imaging millimeter wave radar; wherein the multiple dimensional information includes pitch angle, horizontal angle, Euclidean distance, three-dimensional coordinates, etc.

[0029] It should be noted that the 4D imaging millimeter wave radar of the present invention is installed at the front bumper of the vehicle.

[0030] Step S12: establishing a grid map according to the current dimension information of the target point cloud around the vehicle, wherein the grid map includes a plurality of minimum grids whose grid side lengths are all minimum step lengths;

[0031] It should be noted that the minimum step size MinStep is the side length of the minimum grid of the initial division of the grid map. In the initial division stage, the minimum step size MinStep is used to preliminarily divide the data and establish a grid map. This grid map has a large number of minimum grids because the minimum step size MinStep is much smaller than the normal rasterization step size.

[0032] In the embodiment of the present invention, the minimum step size MinStep is usually set according to the accuracy and characteristics of the radar point cloud. The typical minimum step size MinStep is about 1 / 6 of the fixed step size grid under the same conditions. Taking the angular coordinate method as an example, when the horizontal FOV is ±60°, the fixed minimum step size MinStep is 1°, and the grid map is divided into 120 minimum grids.

[0033] Step S13: traversing all target point clouds around the vehicle, allocating the target point clouds around the vehicle to the corresponding minimum grids on the grid map according to their respective current dimension information, and calculating the target point cloud density in each minimum grid;

[0034] It should be noted that the density of the target point cloud in each minimum grid can be calculated using the number of target point clouds in the minimum grid, or can be calculated using a weighted average method of the target point cloud intensity.

[0035] Step S14: According to the density of the target point cloud in each minimum grid, multiple minimum grids in the grid map are merged to form multiple merged grids; steps S12 to S14 are repeated until all grid maps established by all dimensional information of the target point cloud around the vehicle have completed grid merging.

[0036] like Figure 2A-2B As shown, Figure 2A and 2B The left picture in the figure is the grid divided by the original fixed step length, in which the gray part is the distribution position of the target point cloud around the vehicle, and the blue shadow part is the minimum grid with the density meeting the standard; Figure 2A and 2B The right picture in the figure is the adaptively divided grid (i.e. the merged grid), where the gray part is the distribution position of the target point cloud around the vehicle, and the blue shadow part is the merged grid.

[0037] Preferably, the method of merging a plurality of minimum grids in the grid map according to the target point cloud density in each minimum grid to form a plurality of merged grids further comprises:

[0038] For each minimum grid in the grid map, a density difference ratio between the target point cloud density in the current minimum grid and the target point cloud density in the adjacent minimum grid is calculated;

[0039] Specifically, the step of calculating the density difference ratio between the target point cloud density in the current minimum grid and the target point cloud density in the adjacent minimum grid according to the target point cloud density in the current minimum grid also includes:

[0040] Density difference ratio ρ' n The calculation formula is as follows:

[0041]

[0042] Among them, ρ n Refers to the target point cloud density in the current minimum grid, ρ n-1 Refers to the target point cloud density in the adjacent minimum grid of the current minimum grid.

[0043] The two smallest grids whose density difference ratio is less than the density difference threshold are merged to form a merged grid. The density difference threshold D_rate is set to 0.25.

[0044] It should be noted that the density difference threshold D_rate is used to determine whether two adjacent minimum grids need to be merged. If two adjacent minimum grids need to be merged, first merge their corresponding point cloud indexes to facilitate remapping to points later. After each minimum grid is merged, reorder and recalculate the merged grids of this dimension.

[0045] It should be noted that since the step sizes of multiple merged grids may be different, the density calculation cannot simply use the number of points.

[0046] Common radar point clouds can use this method in three spatial dimensions to form an adaptive grid. It is worth noting that the minimum step size MinStep and the density difference threshold D_rate do not have to be the same in each dimension. For example, for common 4D radar point clouds, the accuracy in the horizontal direction is higher than that in the vertical direction. When using this method, a smaller minimum step size MinStep can be used in the horizontal dimension to accurately describe the point cloud boundary, while a larger minimum step size MinStep can be used in the vertical direction to reduce the amount of calculation, and a smaller density difference threshold D_rate can be used to prevent mismerging.

[0047] The present invention provides an adaptive grid division method based on 4D imaging millimeter wave radar point cloud. Compared with the traditional fixed-step grid, the adaptive grid can identify the gaps that appear in a single dimension. Merging grids of similar density can reduce a large number of redundant grids when the point cloud is sparse, thereby improving computing efficiency. Taking common urban roads as an example, taking the horizontal FOV ±50° and the distance of 300m of the current 4d radar device as an example, its maximum lateral detection distance at 300m can reach about ±230m, which is far beyond the normal road width. Therefore, it is unnecessary to use a fixed-step grid, and a large number of redundant grids will be generated to slow down the subsequent calculation speed. The use of this method can effectively reduce redundant grids and use fewer grids to describe more general information.

[0048] At the same time, since the adaptive grid can dynamically adjust its size according to the actual density, it can better describe the outer contour of the point cloud. Its theoretical grid resolution is equal to the minimum step size MinStep, and the value of the minimum step size MinStep is much smaller than the step size of the fixed step size grid. Therefore, while reducing the amount of calculation, the theoretical accuracy of this method can also reach several times that of the fixed step size method.

[0049] The present invention provides an adaptive grid division method based on 4D imaging millimeter wave radar point cloud, which performs adaptive merging in each dimension, and has better effects on point clouds with uneven density within the dimension and large differences between dimensions. The flexible grid size is also more suitable for scenes with large density differences, such as radar point clouds on actual roads.

[0050] The present invention provides an adaptive grid division method based on 4D imaging millimeter wave radar point cloud, which provides a basis for the subsequent grid clustering algorithm.

[0051] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. An adaptive grid division method based on 4D imaging millimeter wave radar point cloud, characterized in that: The adaptive grid division method based on 4D imaging millimeter wave radar point cloud comprises the following steps: Step S11: acquiring multiple dimensional information of the target point cloud around the vehicle collected by the 4D imaging millimeter wave radar; Step S12: establishing a grid map according to the current dimension information of the target point cloud around the vehicle, wherein the grid map includes a plurality of minimum grids whose grid side lengths are all minimum step lengths; Step S13: allocating the target point clouds around the vehicle to the corresponding minimum grids on the grid map according to their respective current dimension information, and calculating the density of the target point clouds in each minimum grid; Step S14: According to the density of the target point cloud in each minimum grid, multiple minimum grids in the grid map are merged to form multiple merged grids; steps S12 to S14 are repeated until all grid maps established by all dimensional information of the target point cloud around the vehicle have completed grid merging.

2. The adaptive grid division method based on 4D imaging millimeter wave radar point cloud according to claim 1 is characterized in that: The method further comprises: merging a plurality of minimum grids in the grid map according to the target point cloud density in each minimum grid to form a plurality of merged grids; For each minimum grid in the grid map, a density difference ratio between the target point cloud density in the current minimum grid and the target point cloud density in the adjacent minimum grid is calculated; The two smallest rasters whose density difference ratio is less than the density difference threshold are merged to form a merged raster.

3. The adaptive grid division method based on 4D imaging millimeter wave radar point cloud according to claim 2 is characterized in that: The step of calculating the density difference ratio between the target point cloud density in the current minimum grid and the target point cloud density in the adjacent minimum grid according to the target point cloud density in the current minimum grid also includes: Density difference ratio ρ' n The calculation formula is as follows: Among them, ρ n Refers to the target point cloud density in the current minimum grid, ρ n-1 Refers to the target point cloud density in the adjacent minimum grid of the current minimum grid.

4. The adaptive grid division method based on 4D imaging millimeter wave radar point cloud according to claim 2 is characterized in that: The value of the density difference threshold D_rate is 0.25.

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