Recognition of traversable areas for mobile robotic navigation in outdoor environments

In this paper we consider the problem of automatically determining whether regions in an outdoor environment can be traversed by a mobile robot. We propose a two-level classifier that uses data from a single color image to make this determination. At the low level, we have implemented three classifiers based on color histograms, directional filters and local binary patterns. The outputs of these low level classifiers are combined using a voting scheme that weights the results of each classifier using an estimate of its error probability. We present results from a large number of trials using a database of representative images acquired in real outdoor environments.

[1]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[2]  Eric Krotkov,et al.  Natural terrain hazard detection with a laser rangefinder , 1997, Proceedings of International Conference on Robotics and Automation.

[3]  Eduardo Nebot,et al.  Localization and map building using laser range sensors in outdoor applications , 2000, J. Field Robotics.

[4]  Avinash C. Kak,et al.  Vision for Mobile Robot Navigation: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  James P. Ostrowski,et al.  Visual motion planning for mobile robots , 2002, IEEE Trans. Robotics Autom..

[6]  E. Nebot,et al.  Autonomous Navigation and Map building Using Laser Range Sensors in Outdoor Applications , 2000 .

[7]  Michel Devy,et al.  Natural scene understanding for mobile robot navigation , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[8]  Alonzo Kelly,et al.  Obstacle detection for unmanned ground vehicles: a progress report , 1995 .

[9]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[10]  B. Kimia,et al.  3D object recognition using shape similiarity-based aspect graph , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[11]  Rafael Murrieta-Cid,et al.  Building multi-level models: from landscapes to landmarks , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[12]  Sebastian Thrun,et al.  Bayesian Landmark Learning for Mobile Robot Localization , 1998, Machine Learning.

[13]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Larry H. Matthies Toward stochastic modeling of obstacle detectability in passive stereo range imagery , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  David J. Kriegman,et al.  Vision-based motion planning and exploration algorithms for mobile robots , 1995, IEEE Trans. Robotics Autom..

[16]  Farzin Mokhtarian,et al.  Silhouette-Based Isolated Object Recognition through Curvature Scale Space , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  D. W. Thompson,et al.  Three-dimensional model matching from an unconstrained viewpoint , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[18]  David G. Stork,et al.  Pattern Classification , 1973 .

[19]  Ricardo Swain Oropeza,et al.  Sensor-based navigation in cluttered environments , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[20]  J. Koenderink,et al.  The internal representation of solid shape with respect to vision , 1979, Biological Cybernetics.

[21]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[23]  M. Hebert,et al.  Finding Images of Landmarks in Video Sequences , .