Visual odometer system to build feature based maps for mobile robot navigation

This paper presents a visual odometer system for mobile robot position correction. The developed algorithm detects the same Speeded Up Robust Features (SURF) on the stereo pair images to obtain three dimensional point clouds at every robot location. The algorithm tracks the displacement of the identical features viewed from different positions to compute the robots positions. The displacements between the point clouds are computed with the use of the Iterative Closest Point (ICP) algorithm. The ICP is used also to register the landmarks in the feature based map of the entire environment. The results of an indoor office environment experiments are shown.

[1]  James J. Little,et al.  Vision-based global localization and mapping for mobile robots , 2005, IEEE Transactions on Robotics.

[2]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Juan D. Tardós,et al.  Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision , 2008, IEEE Transactions on Robotics.

[4]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[5]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[6]  Trevor Darrell,et al.  Using Multiple-Hypothesis Disparity Maps and Image Velocity for 3-D Motion Estimation , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[7]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[9]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  Kurt Konolige,et al.  Large-Scale Visual Odometry for Rough Terrain , 2007, ISRR.

[11]  Trevor Darrell,et al.  Motion Estimation from Disparity Images , 2001, ICCV.