One of the main components of an autonomous vehicle is the obstacle detection pipeline. Most prototypes, both from research and industry, rely on lidars for this task. Point-cloud information from lidar is usually combined with data from cameras and radars, but the backbone of the architecture is mainly based on 3D bounding boxes computed from lidar data. To retrieve an accurate representation, sensors with many planes, e.g., greater than 32 planes, are usually employed. The returned pointcloud is indeed dense and well defined, but high-resolution sensors are still expensive and often require powerful GPUs to be processed. Lidars with fewer planes are cheaper, but the returned data are not dense enough to be processed with state of the art deep learning approaches to retrieve 3D bounding boxes. In this paper, we propose two solutions based on occupancy grid and geometric refinement to retrieve a list of 3D bounding boxes employing lidar with a low number of planes (i.e., 16 and 8 planes). Our solutions have been validated on a custom acquired dataset with accurate ground truth to prove its feasibility and accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Two algorithms for vehicular obstacle detection in sparse pointcloud


    Contributors:


    Publication date :

    2021-11-17


    Size :

    856108 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    VEHICULAR OBSTACLE DETECTION DEVICE, AND VEHICULAR OBSTACLE DETECTION SYSTEM

    NAKANO FUMITAKE / AZUMA NAOYA | European Patent Office | 2016

    Free access

    Vehicular obstacle detection device, and vehicular obstacle detection system

    NAKANO FUMITAKE / AZUMA NAOYA | European Patent Office | 2015

    Free access

    VEHICULAR OBSTACLE DETECTION DEVICE

    IEDA SEIICHI | European Patent Office | 2018

    Free access

    High-fidelity LiDAR Simulation System Based on Real Pointcloud Features

    Yang, Xiaoke / Zhang, Yong / Wang, Yafei et al. | IEEE | 2023