In recent years some direct monocular SLAM methods have appeared achieving impressive semi-dense or dense 3D scene reconstruction. At the same time, feature-based monocular SLAM methods can obtain more accurate trajectory than direct methods, but only obtain sparse feature point map rather than semi-dense or even dense map like direct methods. With the development of deep learning, it becomes possible to predict the depth map of a scene given a single RGB image. In this paper we demonstrate how depth prediction module via deep learning can be used as a plug-in module in highly accurate feature-based monocular SLAM (e.g. ORB-SLAM). Both accurate trajectory from ORB-SLAM and dense 3D reconstruction from depth prediction can be achieved. Evaluation results show that dense scene reconstruction can be obtained from highly accuarate feature-based monocular SLAM.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dense Mapping from Feature-Based Monocular SLAM Based on Depth Prediction


    Contributors:
    Duan, Yongli (author) / Zhang, Jing (author) / Yang, Lingyu (author)


    Publication date :

    2018-08-01


    Size :

    443552 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    ORB-SLAM based semi-dense mapping with monocular camera

    Wang, Boshi / Wang, Haiying / Yu, Yuan et al. | IEEE | 2017


    Fast semi-dense 3D semantic mapping with monocular visual SLAM

    Li, Xuanpeng / Ao, Huanxuan / Belaroussi, Rachid et al. | IEEE | 2017


    Sensitivity study for feature-based monocular 3D SLAM

    Bergstrom, Niklas / Raabe, Chris / Saito, Kenjiro et al. | IEEE | 2015



    Vision-Based SLAM: Stereo and Monocular Approaches

    Lemaire, T. / Berger, C. / Jung, I. K. et al. | British Library Online Contents | 2007