Indoor SLAM using a range-augmented omnidirectional vision

A promising combination of sensors as a laser range finder and an omnidirectional camera can be used to extract rich 3D information from indoor environments for robot mapping and localization. This paper presents an implementation of FastSLAM using 3D vertical edges retrieved from a range-augmented omnidirectional vision sensor. Our sensor model in conjunction with the FastSLAM algorithm solves the indoor Simultaneous Localization and Mapping problem. The real world experiment to validate our approach used a Pioneer-3DX mobile robot equipped with a URG-04LX laser range finder and an omnidirectional camera.

[1]  Xavier Cufí,et al.  Embedding range information in omnidirectional images through laser range finder , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Juan D. Tardós,et al.  Data association in stochastic mapping using the joint compatibility test , 2001, IEEE Trans. Robotics Autom..

[3]  Davide Scaramuzza,et al.  Omnidirectional Vision: From Calibration to Root Motion Estimation , 2007 .

[4]  Frank Dellaert,et al.  Online probabilistic topological mapping , 2011, Int. J. Robotics Res..

[5]  Patrick Rives,et al.  Calibration between a central catadioptric camera and a laser range finder for robotic applications , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

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

[7]  Paul Newman,et al.  Highly scalable appearance-only SLAM - FAB-MAP 2.0 , 2009, Robotics: Science and Systems.

[8]  Eduardo Mario Nebot,et al.  Recursive scan-matching SLAM , 2007, Robotics Auton. Syst..

[9]  Christopher Mei.  Laser-augmented omnidirectional vision for 3D localisation and mapping , 2007 .

[10]  Attawith Sudsang,et al.  Bearing only FastSLAM using vertical line information from an omnidirectional camera , 2009, 2008 IEEE International Conference on Robotics and Biomimetics.

[11]  Kostas Daniilidis,et al.  A Unifying Theory for Central Panoramic Systems and Practical Applications , 2000, ECCV.

[12]  Patrick Rives,et al.  An Efficient Direct Approach to Visual SLAM , 2008, IEEE Transactions on Robotics.

[13]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Patrick Rives,et al.  Appearance-based SLAM relying on a hybrid laser/omnidirectional sensor , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[16]  Joaquim Salvi,et al.  Appearance-based mapping and localization for mobile robots using a feature stability histogram , 2011, Robotics Auton. Syst..

[17]  Sebastian Thrun,et al.  Simultaneous localization and mapping with unknown data association using FastSLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[18]  Pascal Vasseur,et al.  Rectangle Extraction in Catadioptric Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.