A spherical representation for efficient visual loop closing

We present a novel approach to deal with visual loop closure detection independently from the point of view in large scale environments. Loop closure detection is the process of recognizing when a robot comes back to a previously visited location and is a key issue for place recognition, topological localization and visual Simultaneous Localization And Mapping. Our solution relies on an ego-centred spherical representation of the environment and the exploitation of two kinds of information: local appearance and global feature density. Local appearance is provided by local features extracted from the spherical representation and processed using the bag of visual words approach. Global density information is computed from the distribution over the sphere of these features thus characterizing the environment structure. The approach is purely appearance-based and does not involve any geometric information such as epipolar constraints. An experiment on an 1.5kms trajectory in an environment containing buildings and vegetation validates our algorithm and shows a reduction of the false alarm rate by about 50% for the same probability of detection compared to the standard bag of visual words approach.

[1]  Paolo Pirjanian,et al.  The vSLAM Algorithm for Robust Localization and Mapping , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[2]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[3]  J. Barreto.  General central projection systems: modeling, calibration and visual servoing , 2004 .

[4]  Ankita Kumar,et al.  Experiments on visual loop closing using vocabulary trees , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[5]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Jean-Arcady Meyer,et al.  Real-time visual loop-closure detection , 2008, 2008 IEEE International Conference on Robotics and Automation.

[7]  Michael Bosse,et al.  Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework , 2004, Int. J. Robotics Res..

[8]  Shree K. Nayar,et al.  Catadioptric omnidirectional camera , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Vincent Lepetit,et al.  View-based Maps , 2010, Int. J. Robotics Res..

[10]  Jean-Arcady Meyer,et al.  Incremental vision-based topological SLAM , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[12]  Richard Szeliski,et al.  Street slide: browsing street level imagery , 2010, ACM Trans. Graph..

[13]  Emanuele Menegatti,et al.  Image-based memory for robot navigation using properties of omnidirectional images , 2004, Robotics Auton. Syst..

[14]  Patrick Rives,et al.  Dense visual mapping of large scale environments for real-time localisation , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[16]  P. Newman,et al.  SLAM in large-scale cyclic environments using the Atlas framework , 2003 .

[17]  Grzegorz Cielniak,et al.  An Adaptive Spherical View Representation for Navigation in Changing Environments , 2009, ECMR.

[18]  Luc Van Gool,et al.  Omnidirectional Vision Based Topological Navigation , 2007, International Journal of Computer Vision.

[19]  Luc Vincent,et al.  Taking Online Maps Down to Street Level , 2007, Computer.

[20]  Stergios I. Roumeliotis,et al.  Two Efficient Solutions for Visual Odometry Using Directional Correspondence , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Jean-Arcady Meyer,et al.  Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words , 2008, IEEE Transactions on Robotics.

[22]  Kostas Daniilidis,et al.  Catadioptric Projective Geometry , 2001, International Journal of Computer Vision.

[23]  Jana Kosecka,et al.  Experiments in place recognition using gist panoramas , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[24]  Shree K. Nayar,et al.  Towards a true spherical camera , 2009, Electronic Imaging.

[25]  Friedrich Fraundorfer,et al.  Topological mapping, localization and navigation using image collections , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Robert Pless,et al.  A spherical eye from multiple cameras (makes better models of the world) , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[27]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[28]  Wolfram Burgard,et al.  Efficient estimation of accurate maximum likelihood maps in 3D , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[30]  Paul Newman,et al.  FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance , 2008, Int. J. Robotics Res..

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