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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Additional title:

    Sae Technical Papers


    Contributors:
    Li, Bin (author) / Lu, Fan (author) / Chen, Guang (author) / Xue, Shijie (author) / Liu, Zhengfa (author)

    Conference:

    SAE 2023 Intelligent and Connected Vehicles Symposium ; 2023



    Publication date :

    2023-12-20




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English





    Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

    Zhu, Jianliang / Ai, Yunfeng / Tian, Bin et al. | IEEE | 2018


    Incremental Cross-Modality deep learning for pedestrian recognition

    Pop, Danut Ovidiu / Rogozan, Alexandrina / Nashashibi, Fawzi et al. | IEEE | 2017


    ImageNet Large Scale Visual Recognition Challenge

    Russakovsky, O. / Deng, J. / Su, H. et al. | British Library Online Contents | 2015