Outdoor visual path following experiments

In this paper the performance of a topological- metric visual path following framework is investigated in different environments. The framework relies on a monocular camera as the only sensing modality. The path is represented as a series of reference images such that each neighboring pair contains a number of common landmarks. Local 3D geometries are reconstructed between the neighboring reference images in order to achieve fast feature prediction which allows the recovery from tracking failures. During navigation the robot is controlled using image-based visual servoing. The experiments show that the framework is robust against moving objects and moderate illumination changes. It is also shown that the system is capable of on-line path learning.

[1]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[2]  Simon Lacroix,et al.  Vision-Based SLAM: Stereo and Monocular Approaches , 2007, International Journal of Computer Vision.

[3]  Andrew Zisserman,et al.  MLESAC: A New Robust Estimator with Application to Estimating Image Geometry , 2000, Comput. Vis. Image Underst..

[4]  Patrick Gros,et al.  3D navigation based on a visual memory , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[5]  Stefano Soatto,et al.  Real-time feature tracking and outlier rejection with changes in illumination , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Masayuki Inaba,et al.  Visual navigation using view-sequenced route representation , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[7]  Todd Jochem,et al.  Rapidly Adapting Machine Vision for Automated Vehicle Steering , 1996, IEEE Expert.

[8]  Zhichao Chen,et al.  Qualitative vision-based mobile robot navigation , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[9]  Larry H. Matthies,et al.  Visual odometry on the Mars exploration rovers - a tool to ensure accurate driving and science imaging , 2006, IEEE Robotics & Automation Magazine.

[10]  Sinisa Segvic,et al.  Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support , 2006, ECCV.

[11]  Luc Van Gool,et al.  Feature based omnidirectional sparse visual path following , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Sinisa Segvic,et al.  Large scale vision-based navigation without an accurate global reconstruction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Sebastian Thrun,et al.  A Personal Account of the Development of Stanley, the Robot That Won the DARPA Grand Challenge , 2006, AI Mag..

[14]  Michel Dhome,et al.  Outdoor autonomous navigation using monocular vision , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[16]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[17]  Udo Frese,et al.  Closing a Million-Landmarks Loop , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

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