| Citation: | NIU G C，WANG Y Y，TIAN Y B. LiDAR obstacle detection based on improved density clustering[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（10）：2608-2616 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0733
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For the 3D point cloud collected by the LiDAR on the intelligent vehicle in the park environment, there exist some problems, for example, the obstacles far away from the LiDAR are easily missed, adjacent obstacles are prone to incompletely or excessively differentiate, and the algorithm is time-consuming. To solve these problems, the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed by adaptively changing the clustering radius based on laser beams that are distributed differently in the horizontal and vertical directions. It is also suggested to combine the upgraded k-means algorithm with the concept of group clustering to create a quick and accurate obstacle identification technique. Firstly, the 3D point clouds in the region of interest (ROI) are preliminarily grouped by using the improved k-means algorithm according to 3D point clouds density characteristics. Then, the point clouds in each group are clustered in parallel using the parameter adaptive DBSCAN algorithm. Finally, the clusters on the qualified group boundary are merged to complete the obstacle detection. The experimental findings indicate that when compared to the conventional approaches, the suggested method’s true positive rate of obstacle detection is enhanced by 17.5%, and the average time consumption is decreased by 23.6%.
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