In this study, we propose a novel visual localization approach to accurately estimate six degrees of freedom (6-DoF) poses of the robot within the 3D LiDAR map based on visual data from an RGB camera. The 3D map is obtained utilizing an advanced LiDAR-based simultaneous localization and mapping (SLAM) algorithm capable of collecting a precise sparse map. The features extracted from the camera images are compared with the points of the 3D map, and then the geometric optimization problem is being solved to achieve precise visual localization. Our approach allows employing a scout robot equipped with an expensive LiDAR only once — for mapping of the environment, and multiple operational robots with only RGB cameras onboard — for performing mission tasks, with the localization accuracy higher than common camera-based solutions. The proposed method was tested on the custom dataset collected in the Skolkovo Institute of Science and Technology (Skoltech). During the process of assessing the localization accuracy, we managed to achieve centimeter-level accuracy; the median translation error was as low as 1.3 cm. The precise positioning achieved with only cameras makes possible the usage of autonomous mobile robots to solve the most complex tasks that require high localization accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    CloudVision: DNN-based Visual Localization of Autonomous Robots using Prebuilt LiDAR Point Cloud




    Publication date :

    2023-06-01


    Size :

    3286151 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Visual Localization for Autonomous Driving using Pre-built Point Cloud Maps

    Yabuuchi, Kento / Wong, David Robert / Ishita, Takeshi et al. | IEEE | 2021


    Increasing Conceptual Design Fidelity with Prebuilt Airfoil Databases

    MacDonald, Timothy / Alonso, Juan J. | AIAA | 2018


    3D LIDAR point cloud based intersection recognition for autonomous driving

    Zhu, Quanwen / Chen, Long / Li, Qingquan et al. | IEEE | 2012


    3D LIDAR Point Cloud Based Intersection Recognition for Autonomous Driving

    Zhu, Q. / Chen, L. / Li, Q. et al. | British Library Conference Proceedings | 2012


    POINT CLOUD SEGMENTATION USING A COHERENT LIDAR FOR AUTONOMOUS VEHICLE APPLICATIONS

    ARMSTRONG-CREWS NICHOLAS / CHEN MINGCHENG / HU XIAOXIANG | European Patent Office | 2023

    Free access