交通による遮蔽に強いLiDAR横方向自己位置推定
LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic
車両に遮られて見えない道路境界をディープニューラルネットで推定し、LiDARによる横方向の自己位置推定の頑健性を向上させた研究。オックスフォード中心部40kmの走行で性能改善を実証した。
著者: Tarlan Suleymanov, Matthew Gadd, Lars Kunze, Paul Newman
分類: cs.RO
原文アブストラクト
This paper presents a system for improving the robustness of LiDAR lateral localisation systems. This is made possible by including detections of road boundaries which are invisible to the sensor (due to occlusion, e.g. traffic) but can be located by our Occluded Road Boundary Inference Deep Neural Network. We show an example application in which fusion of a camera stream is used to initialise the lateral localisation. We demonstrate over four driven forays through central Oxford - totalling 40 km of driving - a gain in performance that inferring of occluded road boundaries brings.
