Due to the difference of data modalities, it’s a very challenging task to find the feature correspondences between 2D and 3D data in LiDAR-Camera calibration. In existing works, the establishment of the cross-model correspondence is always simplified by specifically designing artificial targets or restricting the region of searching correspondences with the help of initial extrinsic parameters. To achieve automatic LiDAR-Camera calibration without prior knowledge, we propose a novel self-adaptive LiDAR-Camera calibration approach named ATOP which realizes a cascaded procedure of ATtention-to-OPtimization. In the attention stage, an attention-based object-level matching network called Cross-Modal Matching Network (CMON) is designed for finding the overlapped FOV(Field of View) between camera and LiDAR, and producing 2D-3D object-level correspondences. In the optimization stage, two cascaded PSO-based (Particle Swarm Optimization) algorithms, namely Point-PSO and Pose-PSO, are designed to estimate the LiDAR-Camera extrinsic parameters. Different from previous works, the proposed calibration method does not require any artificial targets or initial pose guesses, therefore it can be applied to achieve online self-adaptive LiDAR-Camera calibration. Besides, this is the first work, to our best knowledge, to achieve object-level matching between uncalibrated camera and LiDAR data. Experimental results on both the collected datasets and KITTI datasets demonstrate the effectiveness of the proposed method.


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    Titel :

    ATOP: An Attention-to-Optimization Approach for Automatic LiDAR-Camera Calibration via Cross-Modal Object Matching


    Beteiligte:
    Sun, Yi (Autor:in) / Li, Jian (Autor:in) / Wang, Yuru (Autor:in) / Xu, Xin (Autor:in) / Yang, Xiaohui (Autor:in) / Sun, Zhenping (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-01-01


    Format / Umfang :

    7711872 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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