Curb Detection Using 2D Range Data in a Campus Environment

Curb detection is an important research topic for unmanned ground vehicle (UGV) navigation. In this paper, a new curb detection method is proposed using a 2D laser range finder in a campus environment. Firstly, a local Digital Elevation Map (DEM) is built with 2D sequential laser range finder data and vehicle state information. Then, the curb candidate points are extracted considering the moving direction of the vehicle in the local DEM. Finally, the 1D Gaussian process regression is firstly used for curb detection, and the initial training curb data are obtained online by the extracted straight curbs. The proposed method has been verified in different scenes with the real vehicle platform, and it can detect the straight and curved curbs robustly in a campus environment.

[1]  Slađana Mladenović.  ODNOS DRŽAVE I NEVLADINOG SEKTORA U PROCESU EVROPSKIH INTEGRACIJA SRBIJE , 2012 .

[2]  L. Smadja,et al.  ROAD EXTRACTION AND ENVIRONMENT INTERPRETATION FROM LIDAR SENSORS , 2010 .

[3]  W. Sardha Wijesoma,et al.  Road-boundary detection and tracking using ladar sensing , 2004, IEEE Transactions on Robotics and Automation.

[4]  Wolfram Burgard,et al.  World Modeling , 2008, Springer Handbook of Robotics.

[5]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[6]  Haofeng Zhang,et al.  Road Detection and Corner Extraction Using High Definition Lidar , 2010 .

[7]  W. Sardha Wijesoma,et al.  Road curb and intersection detection using a 2D LMS , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Roberto Manduchi,et al.  CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data , 2011, Pattern Recognit. Lett..

[9]  Wolfgang Förstner,et al.  Curb reconstruction using Conditional Random Fields , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[10]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[11]  Sergiu Nedevschi,et al.  Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[12]  Roberto Manduchi,et al.  Robust curb and ramp detection for safe parking using the Canesta TOF camera , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[13]  Sergiu Nedevschi,et al.  Polynomial curb detection based on dense stereovision for driving assistance , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.