Open AccessThis article is
- freely available
A New Curb Detection Method for Unmanned Ground Vehicles Using 2D Sequential Laser Data
College of Mechatronics & Automation, National University of Defense Technology, Changsha 410073, Hunan, China
School of Surveying and Geospatial Engineering, the University of New South Wales, Sydney 2052, Australia
* Author to whom correspondence should be addressed.
Received: 12 November 2012; in revised form: 26 December 2012 / Accepted: 9 January 2013 / Published: 16 January 2013
Abstract: Curb detection is an important research topic in environment perception, which is an essential part of unmanned ground vehicle (UGV) operations. In this paper, a new curb detection method using a 2D laser range finder in a semi-structured environment is presented. In the proposed method, firstly, a local Digital Elevation Map (DEM) is built using 2D sequential laser rangefinder data and vehicle state data in a dynamic environment and a probabilistic moving object deletion approach is proposed to cope with the effect of moving objects. Secondly, the curb candidate points are extracted based on the moving direction of the vehicle in the local DEM. Finally, the straight and curved curbs are detected by the Hough transform and the multi-model RANSAC algorithm, respectively. The proposed method can detect the curbs robustly in both static and typical dynamic environments. The proposed method has been verified in real vehicle experiments.
Keywords: curb detection; laser range finder; mapping; dynamic environment
Citations to this Article
Cite This Article
MDPI and ACS Style
Liu, Z.; Wang, J.; Liu, D. A New Curb Detection Method for Unmanned Ground Vehicles Using 2D Sequential Laser Data. Sensors 2013, 13, 1102-1120.
Liu Z, Wang J, Liu D. A New Curb Detection Method for Unmanned Ground Vehicles Using 2D Sequential Laser Data. Sensors. 2013; 13(1):1102-1120.
Liu, Zhao; Wang, Jinling; Liu, Daxue. 2013. "A New Curb Detection Method for Unmanned Ground Vehicles Using 2D Sequential Laser Data." Sensors 13, no. 1: 1102-1120.