Title :
3D LIDAR-based ground segmentation
Author :
Tongtong, Chen ; Bin, Dai ; Daxue, Liu ; Bo, Zhang ; Qixu, Liu
Author_Institution :
Coll. of Mechatron. Eng. & Autom., Nat. Univ. of Defense Technol., Changsha, China
Abstract :
Obtaining a comprehensive model of large and complex ground typically is crucial for autonomous driving both in urban and countryside environments. This paper presents an improved ground segmentation method for 3D LIDAR point clouds. Our approach builds on a polar grid map, which is divided into some sectors, then 1D Gaussian process (GP) regression model and Incremental Sample Consensus (INSAC) algorithm is used to extract ground for every sector. Experiments are carried out at the autonomous vehicle in different outdoor scenes, and results are compared to those of the existing method. We show that our method can get more promising performance.
Keywords :
Gaussian processes; image segmentation; optical radar; radar imaging; regression analysis; road vehicles; robot vision; telerobotics; 1D Gaussian process regression model; 3D LIDAR point clouds; 3D LIDAR-based ground segmentation; GP model; INSAC algorithm; autonomous driving; autonomous vehicle; countryside environments; different outdoor scenes; incremental sample consensus algorithm; polar grid map; urban environments; Classification algorithms; Data models; Gaussian processes; Laser radar; Mobile robots; Roads; Three dimensional displays; Gaussian process; INSAC; ground segmentation; point clouds; polar grid map;
Conference_Titel :
Pattern Recognition (ACPR), 2011 First Asian Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4577-0122-1
DOI :
10.1109/ACPR.2011.6166587
