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Overview: LiDAR plays an important role in the field of unmanned driving. Ground filtering is the key technology to separate and extract the ground information according to the point cloud data acquired by LiDAR. First of all, this paper briefly describes the landmark events that vehicle LiDAR scans (VLS) established its position in the field of unmanned driving. According to the classification of mechanical, mixed solid and solid LiDAR, the working principle of each type of VLS is described, and the advantages and disadvantages of each type of VLS are discussed and compared. Secondly, the development of VLS ground filtering algorithms is studied. And the existing algorithms are sorted according to the processing methods of point cloud data. The ground filtering algorithm is divided into four categories: the ground filtering algorithm based on space division, the ground filtering algorithm based on scan lines, the ground filtering algorithm based on local characteristics, and the ground filtering algorithm based on additional information. According to the principles and filtering results of different algorithms, their characteristics, advantages and disadvantages are described. In addition to the above filtering algorithms, some ground filtering algorithms are also introduced. However, the adaptability of these algorithms to VLS point cloud data needs to be further improved. The common evaluation methods and standards of ground filtering accuracy are described to effectively evaluate the filtering results of various algorithms in different situations. There are three evaluation methods of filtering results: the manual calibration method, the visual inspection method, and the random sampling method. Furthermore, there are three evaluation standards for filtering accuracy: the cross table method, the Kappa coefficient method, and the algorithm time/space complexity. In order to show the characteristics of various algorithms, typical algorithms are selected for comparison from the ground filtering algorithm based on spatial division, the ground filtering algorithm based on scan lines and the ground filtering algorithm based on local characteristics. By changing the selected value of parameters, several groups of tests are carried out for each algorithm. The filtering results are arranged in ascending order according to Kappa coefficient, and the influence of parameter changes on the results is analyzed. The accuracy evaluation criteria are used to compare and analyze the optimal filtering results. Finally, the shortcomings of existing VLS ground filtering algorithms are summarized, and the development trend of VLS and VLS ground filtering algorithms is prospected. With the development of the computer technology and machine learning technology, filtering algorithms will be more intelligent and efficient.
Space division methods of point cloud. (a) Local point cloud; (b) Gridding; (c) Voxel partition
Change of scanning lines. (a) Scanning scene simulation of VLS; (b) Horizontal projection of scanning lines
The scene of point cloud data
Calibration results
Filtering results of three typical ground filtering algorithms. (a) Results of growing up algorithm; (b) Results of adjacent points; (c) Results of slope-regional growth; (d) Point cloud of growing up algorithm; (e) Point cloud of adjacent points; (f) Point cloud of slope-regional growth