An obstacle detection method for LIDAR after removing water surface clutter

The use of LIDAR at sea is not only influenced by the light absorption properties of the water surface. The returned point cloud data is sparse, and the resolution is constantly changing. Nevertheless, it also produces invalid point cloud clutter due to the dynamic waves and bubbles on the water surface. For this reason, we propose a method for LIDAR obstacle detection after removing water surface clutter. The preprocessed point cloud data are grouped and sorted, and the DBSCAN algorithm is used to cluster the point clouds within different groups. The surface waves and bubbles are also removed according to the intensity variation values of each clustered point cloud, and then the obtained point clouds are mapped to fit the obstacle point cloud clusters to present a bit-pose closed frame. Simulation experiments and actual marine experimental tests prove that our method can effectively remove surface clutter waves and improve the positive detection rate by 86.36% compared with the conventional DBSCAN algorithm while reducing the false detection rate and the missed detection rate. The rate is improved by 12.91% and 11.71%, respectively, and can detect various water surface obstacles around unmanned vessels stably and accurately in the real environment.