In this paper, a multi-modal 360° framework for 3D object detection and tracking for autonomous vehicles is presented. The process is divided into four main stages. First, images are fed into a CNN network to obtain instance segmentation of the surrounding road participants. Second, LiDAR-to-image association is performed for the estimated mask proposals. Then, the isolated points of every object are processed by a PointNet ensemble to compute their corresponding 3D bounding boxes and poses. Lastly, a tracking stage based on Unscented Kalman Filter is used to track the agents along time. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A wide variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack in a real autonomous driving application.


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    Titel :

    Towards Autonomous Driving: a Multi-Modal 360° Perception Proposal


    Beteiligte:
    Beltran, Jorge (Autor:in) / Guindel, Carlos (Autor:in) / Cortes, Irene (Autor:in) / Barrera, Alejandro (Autor:in) / Astudillo, Armando (Autor:in) / Urdiales, Jesus (Autor:in) / Alvarez, Mario (Autor:in) / Bekka, Farid (Autor:in) / Milanes, Vicente (Autor:in) / Garcia, Fernando (Autor:in)


    Erscheinungsdatum :

    20.09.2020


    Format / Umfang :

    3122329 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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