Robust view matching-based Markov localization in outdoor environments

This paper describes a view-based localization method in outdoor environments. An important issue in view-based localization is to cope with the change of object views due to changes of weather and seasons. We have developed a two-stage SVM-based localization method which exhibits a high localization performance with few parameter tunings. In this paper, we extend the method in the following two ways: (1) adding new object models and visual features to deal with various urban scenes and (2) introducing a Markov localization strategy to utilize the history of movements. The new method can achieve a 100% localization performance in an urban route under a wide variety of conditions. The comparison with local feature-based methods is also discussed.

[1]  Paul Newman,et al.  Probabilistic Appearance Based Navigation and Loop Closing , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[2]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[3]  Yoshiaki Shirai,et al.  Panoramic View-Based Navigation in Outdoor Environments Based on Support Vector Learning , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Wolfram Burgard,et al.  Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization , 2005, IEEE Transactions on Robotics.

[5]  Hugh F. Durrant-Whyte,et al.  Recognising and Modelling Landmarks to Close Loops in Outdoor SLAM , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[6]  Achim J. Lilienthal,et al.  SIFT, SURF and Seasons: Long-term Outdoor Localization Using Local Features , 2007, EMCR.

[7]  Hong Zhang,et al.  Quantitative Evaluation of Feature Extractors for Visual SLAM , 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

[8]  E. Batschelet.  Circular statistics in biology , 1981 .

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

[10]  Wolfram Burgard,et al.  Active Markov localization for mobile robots , 1998, Robotics Auton. Syst..

[11]  R. Zaccaria,et al.  Markov-localization through color features comparison , 2004, Proceedings of the 2004 IEEE International Symposium on Intelligent Control, 2004..

[12]  Richard Hartley,et al.  Visual localization and loopback detection with a high resolution omnidirectional camera , 2005 .

[13]  Emanuele Menegatti,et al.  Image-based Monte Carlo localisation with omnidirectional images , 2004, Robotics Auton. Syst..

[14]  David M. Bradley,et al.  Real-time image-based topological localization in large outdoor environments , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Yoshiaki Shirai,et al.  View-based localization in outdoor environments based on support vector learning , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Y. Shirai,et al.  A View-Based Outdoor Navigation Using Object Recognition Robust to Changes of Weather and Seasons , 2005 .

[17]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .