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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    Title :

    Seeing Nearby 3D Scenes using Ultrasonic Sensors


    Contributors:


    Publication date :

    2022-06-05


    Size :

    1277326 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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