IEEE We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to extract 3D line and planar primitives from lidar point clouds is presented. This approach overcomes the suboptimality of typical frame-to-frame tracking methods by treating the primitives as landmarks and tracking them over multiple scans. True integration of lidar features with standard visual features and IMU is made possible using a subtle passive synchronization of lidar and camera frames. The lightweight formulation of the 3D features allows for real-time execution on a single CPU. Our proposed system has been tested on a variety of platforms and scenarios, including underground exploration with a legged robot and outdoor scanning with a dynamically moving handheld device, for a total duration of 96 min and 2.4 km traveled distance. In these test sequences, using only one exteroceptive sensor leads to failure due to either underconstrained geometry (affecting lidar) or textureless areas caused by aggressive lighting changes (affecting vision). In these conditions, our factor graph naturally uses the best information available from each sensor modality without any hard switches.


    Access

    Download


    Export, share and cite



    Title :

    Unified multi-modal landmark tracking for tightly coupled lidar-visual-inertial odometry


    Contributors:
    Wisth, D (author) / Camurri, M (author) / Das, S (author) / Fallon, M (author)

    Publication date :

    2022-06-24



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



    Hierarchical Distribution-Based Tightly-Coupled LiDAR Inertial Odometry

    Wang, Chengpeng / Cao, Zhiqiang / Li, Jianjie et al. | IEEE | 2024



    Vehicle localization with tightly coupled GNSS and visual odometry

    Schreiber, Markus / Konigshof, Hendrik / Hellmund, Andre-Marcel et al. | IEEE | 2016


    VEHICLE LOCALIZATION WITH TIGHTLY COUPLED GNSS AND VISUAL ODOMETRY

    Schreiber, Markus / Königshof, Hendrik / Hellmund, André-Marcel et al. | British Library Conference Proceedings | 2016


    Tightly Coupled Kinematics Visual Odometry/PPP System for Land Vehicle Navigation

    Liu, Fei / Yang, Hongzhou / Gao, Yang | British Library Conference Proceedings | 2016