Localization and Mapping for Autonomous Navigation in Outdoor Terrains : A Stereo Vision Approach

We consider the problem of autonomous navigation in unstructured outdoor terrains using vision sensors. The goal is for a robot to come into a new environment, map it and move to a given goal at modest speeds (1 m/sec). The biggest challenges are in building good maps and keeping the robot well localized as it advances towards the goal. In this paper, we concentrate on showing how it is possible to build a consistent, globally correct map in real time, using efficient precise stereo algorithms for map making and visual odometry for localization. While we have made advances in both localization and mapping using stereo vision, it is the integration of the techniques that is the biggest contribution of the research. The validity of our approach is tested in blind experiments, where we submit our code to an independent testing group that runs and validates it on an outdoor robot

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[3]  Kurt Konolige,et al.  Small Vision Systems: Hardware and Implementation , 1998 .

[4]  Kurt Konolige,et al.  Incremental mapping of large cyclic environments , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[5]  Steven Dubowsky,et al.  Rapid physics-based rough-terrain rover planning with sensor and control uncertainty , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[6]  Clark F. Olson,et al.  Robust stereo ego-motion for long distance navigation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Eduardo Mario Nebot,et al.  High accuracy navigation using laser range sensors in outdoor applications , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[8]  Roberto Manduchi,et al.  Terrain perception for DEMO III , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[9]  Ray Jarvis,et al.  3D Vision for Large-Scale Outdoor Environments , 2002 .

[10]  John J. Leonard,et al.  Consistent, Convergent, and Constant-Time SLAM , 2003, IJCAI.

[11]  Sebastian Thrun,et al.  Large-Scale Robotic 3-D Mapping of Urban Structures , 2004, ISER.

[12]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[13]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[14]  Kurt Konolige,et al.  Real-Time Detection of Independent Motion using Stereo , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[15]  A. Rankin,et al.  Evaluation of stereo vision obstacle detection algorithms for off-road autonomous navigation , 2005 .

[16]  Kurt Konolige,et al.  Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS , 2006, 18th International Conference on Pattern Recognition (ICPR'06).