Robotics: Science and Systems I

σMCL: Monte-Carlo Localization for Mobile Robots with Stereo Vision

Pantelis Elinas, James J. Little

Abstract: This paper presents Monte-Carlo localization (MCL) [1] with a mixture proposal distribution for mobile robots with stereo vision. We combine filtering with the Scale Invariant Feature Transform (SIFT) image descriptor to accurately and efficiently estimate the robot's location given a map of 3D point landmarks. Our approach completely decouples the motion model from the robot's mechanics and is general enough to solve for the unconstrained 6-degrees of freedom camera motion. We call our approach σMCL. Compared to other MCL approaches σMCL is more accurate, without requiring that the robot move large distances and make many measurements. More importantly our approach is not limited to robots constrained to planar motion. Its strength is derived from its robust vision-based motion and observation models. σMCL is general, robust, efficient and accurate, utilizing the best of Bayesian filtering, invariant image features and multiple view geometry techniques.



    AUTHOR    = {Pantelis Elinas and James J. Little},
    TITLE     = {$\sigma${MCL: Monte-Carlo} Localization for Mobile
                 Robots with Stereo Vision},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems},
    YEAR      = {2005},
    ADDRESS   = {Cambridge, USA},
    MONTH     = {June},
    DOI       = {10.15607/RSS.2005.I.049}