We propose an algorithm for dense and direct large-scale visual SLAM that runs in real-time on a commodity notebook. A fast variational dense 3D reconstruction algorithm was developed which robustly integrates data terms from multiple images. This mitigates the effect of the aperture problem and is demonstrated on synthetic and real data. An additional property of the variational reconstruction framework is the ability to integrate sparse depth priors (e.g. from RGB-D sensors or LiDAR data) into the early stages of the visual depth reconstruction, leading to an implicit sensor fusion scheme for a variable number of heterogenous depth sensors. Embedded into a keyframe-based SLAM framework, this results in a memory efficient representation of the scene and therefore (in combination with loop-closure detection and pose tracking via direct image alignment) enables us to densely reconstruct large scenes in real-time. Experimental validation on the KITTI dataset shows that our method can recover large-scale and dense reconstructions of entire street scenes in real-time from a driving car.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-time variational stereo reconstruction with applications to large-scale dense SLAM


    Contributors:


    Publication date :

    2017-06-01


    Size :

    3393414 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Real-Time Variational Stereo Reconstruction with Applications to Large-Scale Dense SLAM

    Kuschk, Georg / Bozic, Aljaz / Cremers, Daniel | British Library Conference Proceedings | 2017


    Real-time Omnidirectional Visual SLAM with Semi-Dense Mapping

    Wangl, Senbo / Yuel, Jiguang / Dong, Yanchao et al. | IEEE | 2018


    REAL-TIME OMNIDIRECTIONAL VISUAL SLAM WITH SEMI-DENSE MAPPING

    Wangl, Senbo / Yuel, Jiguang / Dong, Yanchao et al. | British Library Conference Proceedings | 2018


    DENSE 3D SEMANTIC SLAM OF TRAFFIC ENVIRONMENT BASED ON STEREO VISION

    Li, Linhui / Liu, Zhijie / Özgüner, Ümit et al. | British Library Conference Proceedings | 2018


    Dense 3D Semantic SLAM of traffic environment based on stereo vision

    Li, Linhui / Liu, Zhijie / Ozginer, Umit et al. | IEEE | 2018