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

Abstract

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.

3-D Vision for Robot Perception

“…A clustering method is proposed by Wen et al, which combines density-based spatial clustering of applications with noise and 2-D range image composed by scan lines of light detection and ranging based on the order of generation time. 9 The results show that the method achieves state-of-the-art performance in the aspect of time efficiency and clustering accuracy. A ground extraction method based on scan line is also presented in the article.…”

Vehicle Detection Algorithms for Autonomous Driving: A Review

“…Due to the sparse nature of LiDAR point cloud data, it is often necessary to convert 3D LiDAR data to 2D or 2.5D data to improve computational efficiency. These conversion techniques include a graph method [155], range image [156], and occupancy map [157]. Some irrelevant point cloud information can be eliminated to optimize the traffic environment sensing system.…”