A Fast Obstacle Detection Method by Fusion of Density-based Clustering and Region Growing Algorithms

For the 3D point cloud collected by the LiDAR on the intelligent vehicle in urban environment, there exist some problems, for example that the adjacent obstacles are difficult to distinguish, the obstacles far away from the LiDAR are prone to split, and the small obstacles are easily missed. To solve these problems, a robust and fast obstacle detection method is proposed by combining the density-based clustering algorithm and the region growing algorithm. Firstly, the region growing algorithm is used to perform the first clustering of the point cloud grid and marks the grid which may contain smaller obstacles. Then, the parameter adaptive DBSCAN (density-based spatial clustering of applications with noise) algorithm is adopted to refine the detection results of large obstacles which may contain many objects. Finally, the marked grids are clustered by the region growing algorithm for the second time to complete the detection of small obstacles. The experimental results show that the proposed method can accurately distinguish and detect obstacles in urban environment. The detection accuracy is 97% on average, and the average time consumption is 13 ms.