Simultaneous Localization and Mapping (SLAM) and Visual SLAM (V-SLAM) in particular have been an active area of research lately. In V-SLAM the main focus is most often laid on the localization part of the problem allowing for a drift free motion estimate. To this end, a sparse set of landmarks is tracked and their position is estimated. However, this set of landmarks (rendering the map) is often too sparse for tasks in autonomous driving such as navigation, path planning, obstacle avoidance etc. Some methods keep the raw measurements for past robot poses to address the sparsity problem often resulting in a pose only SLAM akin to laser scanner SLAM. For the stereo case, this is however impractical due to the high noise of stereo reconstructed point clouds. In this paper we propose a dense stereo V-SLAM algorithm that estimates a dense 3D map representation which is more accurate than raw stereo measurements. Thereto, we run a sparse V SLAM system, take the resulting pose estimates to compute a locally dense representation from dense stereo correspondences. This dense representation is expressed in local coordinate systems which are tracked as part of the SLAM estimate. This allows the dense part to be continuously updated. Our system is driven by visual odometry priors to achieve high robustness when tracking landmarks. Moreover, the sparse part of the SLAM system uses recently published sub mapping techniques to achieve constant runtime complexity most of the time. The improved accuracy over raw stereo measurements is shown in a Monte Carlo simulation. Finally, we demonstrate the feasibility of our method by presenting outdoor experiments of a car like robot.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Visual SLAM for Autonomous Ground Vehicles


    Beteiligte:
    Lategahn, Henning (Autor:in) / Geiger, Andreas (Autor:in) / Kitt, Bernd (Autor:in)


    Erscheinungsdatum :

    2011


    Format / Umfang :

    6 Seiten, 32 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    A Novel Visual SLAM System for Autonomous Vehicles in Dynamic Environments

    Zeng, Xinyu / He, Ying / Yu, F. Richard et al. | IEEE | 2023


    Stereo Graph-SLAM for Autonomous Underwater Vehicles

    Carrasco, Pep Lluis Negre / Bonin-Font, Francisco / Codina, Gabriel Oliver | Springer Verlag | 2015



    Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM 2 Extension

    Nobis, Felix / Papanikolaou, Odysseas / Betz, Johannes et al. | IEEE | 2020


    Visual SLAM for autonomous MAVs with dual cameras

    Yang, Shaowu / Scherer, Sebastian A. / Zell, Andreas | IEEE | 2014