Terrain mapping and classification using Support Vector Machines

This paper describes a three-dimensional terrain mapping and classification technique to allow the operation of mobile robots in outdoor environments using laser range finders. We use Support Vector Machines to classify portions of mapped terrain into navigable, partially navigable and non-navigable. In order to detect safe places to robot traverse, our approach can be used to assist the robot navigation in unstructured lands. Experimental results obtained using real environments and robot show the efficiency of the presented methods.

[1]  Christopher Rasmussen,et al.  Combining laser range, color, and texture cues for autonomous road following , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[2]  Andreas Zell,et al.  A combination of vision- and vibration-based terrain classification , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Martial Hebert,et al.  Natural terrain classification using three‐dimensional ladar data for ground robot mobility , 2006, J. Field Robotics.

[4]  Thorsten Joachims,et al.  SVM Light: Support Vector Machine , 2002 .

[5]  V. Vapnik Estimation of Dependences Based on Empirical Data , 2006 .

[6]  Wolfram Burgard,et al.  A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[7]  Karsten Berns,et al.  3D obstacle detection and avoidance in vegetated off-road terrain , 2008, 2008 IEEE International Conference on Robotics and Automation.

[8]  Cang Ye,et al.  A new terrain mapping method for mobile robots obstacle negotiation , 2003, SPIE Defense + Commercial Sensing.

[9]  Youngbae Hwang,et al.  Accurate Motion Estimation and High-Precision 3D Reconstruction by Sensor Fusion , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[10]  Robin R. Murphy,et al.  Artificial intelligence and mobile robots: case studies of successful robot systems , 1998 .

[11]  Vladimir Vapnik,et al.  Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics) , 1982 .

[12]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[13]  Koby Crammer,et al.  On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..

[14]  Wolfram Burgard,et al.  Autonomous Terrain Mapping and Classification Using Hidden Markov Models , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.