Robotics: Science and Systems XI

Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM

Raul Mur-Artal, Juan Tardos


In the last years several direct (i.e. featureless) monocular SLAM approaches have appeared showing impressive semi-dense or dense scene reconstructions. These works have questioned the need of features, in which consolidated SLAM techniques of the last decade were based. In this paper we present a novel feature-based monocular SLAM system that is more robust, gives more accurate camera poses, and obtains comparable or better semi-dense reconstructions than the current state of the art. Our semi-dense mapping operates over keyframes, optimized by local bundle adjustment, allowing to obtain accurate triangulations from wide baselines. Our novel method to search correspondences, the measurement fusion and the inter-keyframe depth consistency tests allow to obtain clean reconstructions with very few outliers. Against the current trend in direct SLAM, our experiments show that by decoupling the semi-dense reconstruction from the trajectory computation, the results obtained are better. This opens the discussion on the benefits of features even if a semi-dense reconstruction is desired.



    AUTHOR    = {Raul Mur-Artal AND Juan Tardos}, 
    TITLE     = {Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2015}, 
    ADDRESS   = {Rome, Italy}, 
    MONTH     = {July},
    DOI       = {10.15607/RSS.2015.XI.041}