インフラカメラセンシングにおける物体フットプリントの不確かさ定量化
Object level footprint uncertainty quantification in infrastructure based sensing
インフラ設置カメラで撮影した物体の接地座標の不確かさをカメラ誤差から解析的に推定し、LiDARを真値に誤差サイズを見積もる手法を提案、CARLAで左折時の不確かさ変化を検証した。
著者: Arpan Kusari, Asma Almutairi, Mark E. Gilbert, David J. LeBlanc
分類: cs.RO
原文アブストラクト
We examine the problem of estimating footprint uncertainty of objects imaged using the infrastructure based camera sensing. A closed form relationship is established between the ground coordinates and the sources of the camera errors. Using the error propagation equation, the covariance of a given ground coordinate can be measured as a function of the camera errors. The uncertainty of the footprint of the bounding box can then be given as the function of all the extreme points of the object footprint. In order to calculate the uncertainty of a ground point, the typical error sizes of the error sources are required. We present a method of estimating the typical error sizes from an experiment using a static, high-precision LiDAR as the ground truth. Finally, we present a simulated case study of uncertainty quantification from infrastructure based camera in CARLA to provide a sense of how the uncertainty changes across a left turn maneuver.
