Range image-based density-based spatial clustering of application with noise clustering method of three-dimensional point clouds. (8th March 2018)

• Record Type: Journal Article
• Authors: Wen, Mingyun; Cho, Seoungjae; Chae, Jeongsook; Sung, Yunsick; Cho, Kyungeun
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 2(2018:Mar./Apr.)
• Publication Date: 2018-03-08
• Subjects: Mobile robot -- clustering -- LiDAR -- three-dimensional point cloud -- two-dimensional range image; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418762302
• Languages: English
• ISSNs: 1729-8806

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.