Mobile robot localization using panoramic vision and combinations of feature region detectors

This paper presents a vision-based approach for mobile robot localization. The environmental model is topological. The new approach uses a constellation of different types of affine covariant regions to characterize a place. This type of representation permits a reliable and distinctive environment modeling. The performance of the proposed approach is evaluated using a database of panoramic images from different rooms. Additionally, we compare different combinations of complementary feature region detectors to find the one that achieves the best results. Our experimental results show promising results for this new localization method. Additionally, similarly to what happens with single detectors, different combinations exhibit different strengths and weaknesses depending on the situation, suggesting that a context-aware method to combine the different detectors would improve the localization results.

[1]  Roland Siegwart,et al.  A cognitive modeling of space using fingerprints of places for mobile robot navigation , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[2]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[3]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[4]  Ulrich Nehmzow,et al.  Landmark-based navigation for a mobile robot , 1998 .

[5]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[6]  Richard Hartley,et al.  Localisation using an image-map , 2004 .

[7]  Ramón López de Mántaras,et al.  Comparing combinations of feature regions for panoramic VSLAM , 2007, ICINCO-RA.

[8]  Ben Kröse,et al.  From sensors to rooms. , 2006 .

[9]  José A. Castellanos,et al.  Mobile Robot Localization and Map Building: A Multisensor Fusion Approach , 2000 .

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

[11]  Richard Szeliski,et al.  3-D Scene Data Recovery Using Omnidirectional Multibaseline Stereo , 2004, International Journal of Computer Vision.

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

[13]  Keiji Nagatani,et al.  Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization , 2001, IEEE Trans. Robotics Autom..

[14]  Sebastian Thrun,et al.  Probabilistic Algorithms in Robotics , 2000, AI Mag..

[15]  Arnau Ramisa,et al.  Wide Baseline Stereo Matching Using Voting Schemas , 2006 .

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

[17]  Richard Szeliski,et al.  3-D Scene Data Recovery Using Omnidirectional Multibaseline Stereo , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[19]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[20]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[21]  Tom Duckett,et al.  Localization for Mobile Robots using Panoramic Vision, Local Features and Particle Filter , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[22]  Sebastian Thrun,et al.  A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots , 2001, Int. J. Robotics Res..

[23]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Bernhard Schölkopf,et al.  Learning view graphs for robot navigation , 1997, AGENTS '97.

[25]  S. Basu,et al.  Room-temperature hydrogen sensors based on ZnO , 1997 .

[26]  Benjamin Kuipers,et al.  Towards Autonomous Topological Place Detection Using the Extended Voronoi Graph , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.