低解像度LiDAR点群における車両障害物検出のための2手法
Two algorithms for vehicular obstacle detection in sparse pointcloud
16線・8線の低解像度LiDAR点群から占有格子と幾何学的精緻化により3Dバウンディングボックスを推定する2手法を提案し、独自データセットで有効性を検証した。
著者: Simone Mentasti, Matteo Matteucci, Stefano Arrigoni, Federico Cheli
分類: cs.RO
原文アブストラクト
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. Pointcloud 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.
