Trail following with omnidirectional vision

We describe a system which follows “trails” for autonomous outdoor robot navigation. Through a combination of visual cues provided by stereo omnidirectional color cameras and ladar-based structural information, the algorithm is able to detect and track rough paths despite widely varying tread material, border vegetation, and illumination conditions. The approaching trail region is simply modeled as a circular arc of constant width. Using an adaptive measure of color and brightness contrast between a hypothetical region and flanking areas, the tracker performs a robust randomized search for the most likely trail region and robot pose relative to it with no a priori appearance model. Stereo visual odometry improves tracker dynamics on uneven terrain and permits local obstacle map maintenance. A motion planner is also described which takes the trail shape estimate and local map to plan smooth trajectories around in-trail and near-trail hazards. Our system's performance is analyzed on several long sequences with diverse appearance and structural characteristics using ground-truth segmentations.

[1]  Christopher Rasmussen,et al.  RoadCompass: following rural roads with vision + ladar using vanishing point tracking , 2008, Auton. Robots.

[2]  Karsten Berns,et al.  {RAVON} -- The Robust Autonomous Vehicle for Off-road Navigation , 2009 .

[3]  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).

[4]  Michael Isard,et al.  Active Contours , 2000, Springer London.

[5]  William Whittaker,et al.  A robust approach to high‐speed navigation for unrehearsed desert terrain , 2006, J. Field Robotics.

[6]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[7]  Jitendra Malik,et al.  Recovering human body configurations: combining segmentation and recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[8]  Stan Sclaroff,et al.  Deformable shape detection and description via model-based region grouping , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[9]  Michal Havlena,et al.  Structure from Omnidirectional Stereo Rig Motion for City Modeling , 2008, VISAPP.

[10]  Andrew Y. Ng,et al.  Stereo vision and terrain modeling for quadruped robots , 2009, 2009 IEEE International Conference on Robotics and Automation.

[11]  Kurt Konolige,et al.  Fast color/texture segmentation for outdoor robots , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Sebastian Thrun,et al.  Self-supervised Monocular Road Detection in Desert Terrain , 2006, Robotics: Science and Systems.

[13]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[14]  Alexei A. Efros,et al.  Recovering Surface Layout from an Image , 2007, International Journal of Computer Vision.

[15]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[16]  Alonzo Kelly,et al.  Real-Time, Multi-Perspective Perception for Unmanned Ground Vehicles , 2003 .

[17]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[18]  James R. Bergen,et al.  Visual odometry for ground vehicle applications , 2006, J. Field Robotics.

[19]  Gregory Z. Grudic,et al.  Outdoor Path Labeling Using Polynomial Mahalanobis Distance , 2006, Robotics: Science and Systems.

[20]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Edwin Olson,et al.  Multi-Sensor Lane Finding in Urban Road Networks , 2008, Robotics: Science and Systems.

[22]  Yan Lu,et al.  Appearance contrast for fast, robust trail-following , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Yann LeCun,et al.  Online Learning for Offroad Robots: Spatial Label Propagation to Learn Long-Range Traversability , 2007, Robotics: Science and Systems.

[24]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[25]  Maxim Likhachev,et al.  Motion planning in urban environments: Part I , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Donald Scott,et al.  Shape-guided superpixel grouping for trail detection and tracking , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[28]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[29]  Davide Scaramuzza,et al.  Omnidirectional Vision: From Calibration to Root Motion Estimation , 2007 .

[30]  C. Stachniss,et al.  Online Learning for Offroad Robots: Using Spatial Label Propagation to Learn Long-Range Traversability , 2008 .

[31]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.