The ultrasonic sensors are widely used for vehicles to extract obstacles in the scene. They can obtain the distance to the object directly at a low cost even in harsh environments. However, since the information from a single ultrasonic sensor is very limited, it has not been used for recovering the detailed 3D structure and semantic labels of the scene, unlike in-vehicle cameras or LiDARs. Therefore, we in this paper propose a method for recovering the dense 3D structure and semantic labels of the scene from a moving ultrasonic sensor mounted on a vehicle. Our method uses the raw profiles of the ultrasonic sensor signals and learns the relationship between the raw ultrasonic signals and the 3D scene using multi-task learning. As a result, our method can recover the dense 3D structure and semantic labels of the scene similar to what we would recover with cameras and LiDARs just from a single moving ultrasonic sensor. The efficiency of the proposed method is tested using real sensor data as well as synthetic sensor data.


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    Titel :

    Seeing Nearby 3D Scenes using Ultrasonic Sensors


    Beteiligte:
    Shimoyama, Daina (Autor:in) / Sakaue, Fumihiko (Autor:in) / Kumano, Shunya (Autor:in) / Koyama, Yu (Autor:in) / Matsuura, Mitsuyasu (Autor:in) / Sato, Jun (Autor:in)


    Erscheinungsdatum :

    05.06.2022


    Format / Umfang :

    1277326 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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