Real-Time Hierarchical GPS Aided Visual SLAM on Urban Environments

In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by both a low-cost wide-angle stereo camera and a low-cost GPS. Our approach divides the whole map into local sub-maps identified by the so-called fingerprint (reference poses). At the sub-map level (Low Level SLAM), 3D sequential mapping of natural landmarks and the vehicle location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (High Level SLAM) based on references poses has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. GPS measurements are integrated at both levels, improving global estimation. Some experimental results for different large-scale urban environments are presented, showing an almost constant processing time.

[1]  N. Nathan.  Self and will , 1997 .

[2]  Patrick Rives,et al.  Accurate Quadrifocal Tracking for Robust 3D Visual Odometry , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

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

[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]  Luis Miguel Bergasa,et al.  Real-Time Simultaneous Localization and Mapping using a Wide-Angle Stereo Camera and Adaptive Patches , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Luis Miguel Bergasa,et al.  Real-time wide-angle stereo visual SLAM on large environments using SIFT features correction , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Timothy S. Bailey,et al.  Mobile Robot Localisation and Mapping in Extensive Outdoor Environments , 2002 .

[8]  Paul Newman,et al.  Probabilistic Appearance Based Navigation and Loop Closing , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[9]  Hugh F. Durrant-Whyte.  Uncertain geometry in robotics , 1988, IEEE J. Robotics Autom..

[10]  Peter C. Cheeseman,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[11]  Juan D. Tardós,et al.  Scalable SLAM building conditionally independent local maps , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Gordon Wyeth,et al.  Simultaneous localisation and mapping from natural landmarks using RatSLAM , 2004 .

[13]  Udo Frese.  Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping , 2006, Auton. Robots.

[14]  John J. Leonard,et al.  Robust Mapping and Localization in Indoor Environments Using Sonar Data , 2002, Int. J. Robotics Res..

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

[16]  Tom Duckett,et al.  A multilevel relaxation algorithm for simultaneous localization and mapping , 2005, IEEE Transactions on Robotics.

[17]  Juan D. Tardós,et al.  Hierarchical SLAM: real-time accurate mapping of large environments , 2005, IEEE Transactions on Robotics.

[18]  Randall Smith,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[19]  Tom Drummond,et al.  Monocular SLAM as a Graph of Coalesced Observations , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[21]  Tom Duckett,et al.  Mini-SLAM: Minimalistic Visual SLAM in Large-Scale Environments Based on a New Interpretation of Image Similarity , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[22]  James J. Little,et al.  Vision-based mobile robot localization and mapping using scale-invariant features , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[23]  Michael Bosse,et al.  An Atlas framework for scalable mapping , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[24]  Ian D. Reid,et al.  Mapping Large Loops with a Single Hand-Held Camera , 2007, Robotics: Science and Systems.

[25]  Gordon Wyeth,et al.  Single camera vision-only SLAM on a suburban road network , 2008, 2008 IEEE International Conference on Robotics and Automation.