Range image-based density-based spatial clustering of application with noise clustering method of three-dimensional point clouds:

Clustering plays an important role in processing light detection and ranging points in the autonomous perception tasks of robots. Clustering usually occurs near the start of processing three-dimensional point clouds obtained from light detection and ranging for detection and classification. Therefore, errors caused by clustering will directly affect the detection and classification accuracy. In this article, a clustering method is presented that combines density-based spatial clustering of application with noise and two-dimensional range image composed by scan lines of light detection and ranging based on the order of generation time. The results show that the proposed method achieves state-of-the-art performance in aspect of time efficiency and clustering accuracy. A ground extraction method based on scan line is also presented in this article, which has strong ability to separate ground points and non-ground points.