Efficient Topological Localization Using Global and Local Feature Matching

We present an efficient vision-based global topological localization approach in which different image features are used in a coarse-to-fine matching framework. Orientation Adjacency Coherence Histogram (OACH), a novel image feature, is proposed to improve the coarse localization. The coarse localization results are taken as inputs for the fine localization which is carried out by matching Harris-Laplace interest points characterized by the SIFT descriptor. The computation of OACHs and interest points is efficient due to the fact that these features are computed in an integrated process. The matching of local features is improved by using approximate nearest neighbor searching technique. We have implemented and tested the localization system in real environments. The experimental results demonstrate that our approach is efficient and reliable in both indoor and outdoor environments. This work has also been compared with previous works. The comparison results show that our approach has better performance with higher correct ratio and lower computational complexity.

[1]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[2]  Sebastian Thrun,et al.  Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..

[3]  Avinash C. Kak,et al.  Vision for Mobile Robot Navigation: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Jana Kosecka,et al.  Global localization and relative positioning based on scale-invariant keypoints , 2005, Robotics Auton. Syst..

[5]  Yasushi Yagi,et al.  Iconic memory-based omnidirectional route panorama navigation , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[7]  Yoshiaki Shirai,et al.  A view-based outdoor navigation using object recognition robust to changes of weather and seasons , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[8]  Hans Peter Luhn,et al.  The Automatic Creation of Literature Abstracts , 1958, IBM J. Res. Dev..

[9]  Antonio Torralba,et al.  Context-based vision system for place and object recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Luc Van Gool,et al.  Fast wide baseline matching for visual navigation , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[11]  Antonio Criminisi,et al.  Epitomic Location Recognition , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Illah R. Nourbakhsh,et al.  Appearance-based place recognition for topological localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[13]  Jana Kosecka,et al.  Vision based topological Markov localization , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[14]  Matthew A. Brown,et al.  Recognising panoramas , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

[16]  Adriana Tapus,et al.  Robust Vision-Based Localization using Combinations of Local Feature Regions Detectors , 2009 .

[17]  James J. Little,et al.  Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks , 2002, Int. J. Robotics Res..

[18]  Roberto Cipolla,et al.  An Image-Based System for Urban Navigation , 2004, BMVC.

[19]  Hongbin Zha,et al.  Vision-based Global Localization Using a Visual Vocabulary , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[20]  Hongbin Zha,et al.  Coarse-to-fine vision-based localization by indexing scale-Invariant features , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Wei Zhang,et al.  Localization Based on Building Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[22]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Achim J. Lilienthal,et al.  Incremental spectral clustering and seasons: Appearance-based localization in outdoor environments , 2008, 2008 IEEE International Conference on Robotics and Automation.

[24]  W. Burgard,et al.  Lifelong Localization and Dynamic Map Estimation in Changing Environments , 2010 .

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

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

[27]  Hongbin Zha,et al.  Efficient Topological Localization Using Orientation Adjacency Coherence Histograms , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[28]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.