LiDAR and camera fusion have emerged as a promising approach for improving place recognition in robotics and autonomous vehicles. However, most existing approaches often treat sensors separately, overlooking the potential benefits of correlation between them. In this paper, we propose a Cross- Modality Module (CMM) to leverage the potential correlation of LiDAR and camera features for place recognition. Besides, to fully exploit potential of each modality, we propose a Local-Global Fusion Module to supplement global coarse-grained features with local fine-grained features. The experiment results on public datasets demonstrate that our approach effectively improves the average recall by 2.3%, reaching 98.7%, compared with simply stacking of LiDAR and camera.


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    Titel :

    CMM: LiDAR-Visual Fusion with Cross-Modality Module for Large-Scale Place Recognition


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Li, Bin (Autor:in) / Lu, Fan (Autor:in) / Chen, Guang (Autor:in) / Xue, Shijie (Autor:in) / Liu, Zhengfa (Autor:in)

    Kongress:

    SAE 2023 Intelligent and Connected Vehicles Symposium ; 2023



    Erscheinungsdatum :

    20.12.2023




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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