Point matching and pose optimization are two important processes in LiDAR odometry. The former one is prone to noise and initial pose estimation, while the latter often falls into local minima due to improper matched points. In this paper, we propose a new point pair similarity method in the combination of the normal vector, the smallest eigenvalue of the spatial covariance matrix, and the KL divergence of local intensity values. In pose optimization step, we use both the proposed point pair similarity and planarity as the weight. Experimental results show that it may guarantee higher accuracy with our method and is able to run at 27 FPS on a common PC.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    IGICP: Intensity and Geometry Enhanced LiDAR Odometry


    Beteiligte:
    He, Li (Autor:in) / Li, Wen (Autor:in) / Guan, Yisheng (Autor:in) / Zhang, Hong (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.01.2024


    Format / Umfang :

    5447872 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    InLIOM: Tightly-Coupled Intensity LiDAR Inertial Odometry and Mapping

    Wang, Hanqi / Liang, Huawei / Li, Zhiyuan et al. | IEEE | 2024


    DELIO: DECOUPLED LIDAR ODOMETRY

    Thomas, Queens Maria / Wasenmüller, Oliver / Stricker, Didier | British Library Conference Proceedings | 2019


    LiDAR-Camera Fusion for Depth Enhanced Unsupervised Odometry

    Fetic, Naida / Aydemir, Eren / Unel, Mustafa | IEEE | 2022


    DeLiO: Decoupled LiDAR Odometry

    Thomas, Queens Maria / Wasenmuller, Oliver / Stricker, Didier | IEEE | 2019