Abstract
In this paper, an approach to detecting ground targets using LiDAR point data is proposed. First, outliers are weeded out and point cloud is divided into ground points and non-ground points. Second, the ground surface plane is fitted by ground points and then the relative elevations of all non-ground points are estimated. If the relative elevations of non-ground points exceed a predefined threshold, they will be removed. Subsequently, a 3D region growing algorithm based on the normal vector consistency is employed to generate potential ground targets. Geometric information is used for further filtration of these potential targets on the object level. Finally, the detection performance of the algorithm is analyzed. The experimental results show that the method proposed is effective.
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