Autonomous vision-based exploration and mapping using hybrid maps and Rao-Blackwellised particle filters

This paper addresses the problem of exploring and mapping an unknown environment using a robot equipped with a stereo vision sensor. The main contribution of our work is a fully automatic mapping system that operates without the use of active ranger sensors (such as laser or sonic transducers), can operate in real-time and can consistently produce accurate maps of large-scale environments. Our approach implements a Rao-Blackwellised particle filter (RBPF) to solve the simultaneous localization and mapping problem and uses efficient data structures for real-time data association, mapping, and spatial reasoning. We employ a hybrid map representation that infers 3D point landmarks from image features to achieve precise localization, coupled with occupancy grids for safe navigation. This paper describes our framework and implementation, and presents our exploration method, and experimental results illustrating the functionality of the system

[1]  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.

[2]  Alan C. Schultz,et al.  Mobile robot exploration and map-building with continuous localization , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[3]  Kevin P. Murphy,et al.  Bayesian Map Learning in Dynamic Environments , 1999, NIPS.

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

[5]  Don Ray Murray,et al.  Using Real-Time Stereo Vision for Mobile Robot Navigation , 2000, Auton. Robots.

[6]  Nobuyuki Kita,et al.  3D simultaneous localisation and map-building using active vision for a robot moving on undulating terrain , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

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

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

[10]  Wolfram Burgard,et al.  Exploring Unknown Environments with Mobile Robots using Coverage Maps , 2003, IJCAI.

[11]  Wolfram Burgard,et al.  An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[12]  Sebastian Thrun,et al.  FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges , 2003, IJCAI 2003.

[13]  Ronald Parr,et al.  DP-SLAM 2.0 , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[14]  Gamini Dissanayake,et al.  Multi-Step Look-Ahead Trajectory Planning in SLAM: Possibility and Necessity , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[15]  Hanumant Singh,et al.  Exactly Sparse Delayed-State Filters , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[16]  Nicholas Roy,et al.  Global A-Optimal Robot Exploration in SLAM , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[17]  J. Little,et al.  Scalable real-time vision-based SLAM for planetary rovers , 2005 .

[18]  Wolfram Burgard,et al.  Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM After Actively Closing Loops , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[19]  Wolfram Burgard,et al.  Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[20]  Wolfram Burgard,et al.  Information Gain-based Exploration Using Rao-Blackwellized Particle Filters , 2005, Robotics: Science and Systems.

[21]  James J. Little,et al.  /spl sigma/SLAM: stereo vision SLAM using the Rao-Blackwellised particle filter and a novel mixture proposal distribution , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..