Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this article devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review


    Contributors:
    Cui, Yaodong (author) / Chen, Ren (author) / Chu, Wenbo (author) / Chen, Long (author) / Tian, Daxin (author) / Li, Ying (author) / Cao, Dongpu (author)


    Publication date :

    2022-02-01


    Size :

    5101665 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Point cloud processing for environmental analysis in Autonomous Driving using Deep Learning

    Simon, Martin / Technische Universität Ilmenau / Universitätsverlag Ilmenau | TIBKAT | 2023

    Free access

    Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving

    Abbasi, Rashid / Bashir, Ali Kashif / Alyamani, Hasan J. et al. | IEEE | 2023


    Deep Learning for Autonomous Driving

    Mohamed, Khaled Salah | Springer Verlag | 2023



    Autonomous Driving with Deep Reinforcement Learning

    Zhu, Yuhua / Technische Universität Dresden | SLUB | 2023