3D object detection is an important part of autonomous driving systems, which tasks are to accurately recognize 3D objects around the vehicle, such as cars, pedestrians, and bicycles. Current research is primarily focused on successfully integrating data from camera and LiDAR sensors to enhance detection accuracy and reliability, while overcoming the limits of using a single sensor. In this paper, we provide a review of 3D object detection methods for multi-sensor fusion. First, we introduce common camera and LiDAR sensors and their data processing methods. Subsequently, we classify the fusion algorithms into three categories: input fusion, feature fusion, and late fusion, based on different fusion strategies, and conduct an in-depth survey and discussion on them to analyze their respective advantages and disadvantages. In addition, we provide an overview of public datasets commonly used in 3D object detection. Finally, we provide an outlook on the future direction of multi-sensor fusion 3D object detection technology.


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    Title :

    A review of multi-sensor fusion 3D object detection for autonomous driving


    Contributors:
    Xia, Haojie (editor) / Yu, Liandong (editor) / Zhu, Lianqing (editor) / Luo, Zai (editor) / Zhao, Junwei (author) / Li, Lixiang (author) / Dai, Jun (author)

    Conference:

    Eleventh International Symposium on Precision Mechanical Measurements ; 2023 ; Guangzhou, China


    Published in:

    Proc. SPIE ; 13178


    Publication date :

    2024-09-13





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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