OpenStreetSLAM: Global vehicle localization using OpenStreetMaps

In this paper we propose an approach for global vehicle localization that combines visual odometry with map information from OpenStreetMaps to provide robust and accurate estimates for the vehicle's position. The main contribution of this work comes from the incorporation of the map data as an additional cue into the observation model of a Monte Carlo Localization framework. The resulting approach is able to compensate for the drift that visual odometry accumulates over time, significantly improving localization quality. As our results indicate, the proposed approach outperforms current state-of-the-art visual odometry approaches, indicating in parallel the potential that map data can bring to the global localization task.

[1]  Luc Van Gool,et al.  Robust Multiperson Tracking from a Mobile Platform , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Frank Dellaert,et al.  3D reconstruction of underwater structures , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Luis Miguel Bergasa,et al.  On combining visual SLAM and dense scene flow to increase the robustness of localization and mapping in dynamic environments , 2012, 2012 IEEE International Conference on Robotics and Automation.

[4]  Andreas Geiger,et al.  Visual SLAM for autonomous ground vehicles , 2011, 2011 IEEE International Conference on Robotics and Automation.

[5]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[6]  Bernardo Wagner,et al.  Autonomous robot navigation based on OpenStreetMap geodata , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[7]  Andrew Blake,et al.  Multiscale Categorical Object Recognition Using Contour Fragments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  David Nistér.  A Minimal Solution to the Generalised 3-Point Pose Problem , 2004, CVPR.

[9]  James R. Bergen,et al.  Visual odometry for ground vehicle applications , 2006, J. Field Robotics.

[10]  Sebastian Thrun,et al.  Toward robotic cars , 2010, CACM.

[11]  Robert C. Bolles,et al.  Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching , 1977, IJCAI.

[12]  Rama Chellappa,et al.  Fast directional chamfer matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Daniel P. Huttenlocher,et al.  Distance Transforms of Sampled Functions , 2012, Theory Comput..

[14]  Mubarak Shah,et al.  Accurate Image Localization Based on Google Maps Street View , 2010, ECCV.

[15]  Dariu Gavrila,et al.  Pedestrian Detection from a Moving Vehicle , 2000, ECCV.

[16]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[17]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[18]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[19]  Ahmed M. Elgammal,et al.  A framework for global vehicle localization using stereo images and satellite and road maps , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[20]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Andreas Geiger,et al.  Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[22]  Ian D. Reid,et al.  RSLAM: A System for Large-Scale Mapping in Constant-Time Using Stereo , 2011, International Journal of Computer Vision.

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

[24]  Ahmed M. Elgammal,et al.  Satellite image based precise robot localization on sidewalks , 2012, 2012 IEEE International Conference on Robotics and Automation.