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.


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    Titel :

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


    Beteiligte:
    Cui, Yaodong (Autor:in) / Chen, Ren (Autor:in) / Chu, Wenbo (Autor:in) / Chen, Long (Autor:in) / Tian, Daxin (Autor:in) / Li, Ying (Autor:in) / Cao, Dongpu (Autor:in)


    Erscheinungsdatum :

    01.02.2022


    Format / Umfang :

    5101665 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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