コンパクトなセマンティックマップを用いた単眼カメラによる車両自己位置推定
Monocular Vehicle Self-localization method based on Compact Semantic Map
単眼カメラと軽量な3Dセマンティックマップを使い、深層学習でランドマークを認識して再投影誤差を最小化することで車両の位置・姿勢を高精度に推定する手法を提案した。
著者: Zhongyang Xiao, Kun Jiang, Shichao Xie, Tuopu Wen, Chunlei Yu, Diange Yang
分類: cs.RO
原文アブストラクト
High precision localization is a crucial requirement for the autonomous driving system. Traditional positioning methods have some limitations in providing stable and accurate vehicle poses, especially in an urban environment. Herein, we propose a novel self-localizing method using a monocular camera and a 3D compact semantic map. Pre-collected information of the road landmarks is stored in a self-defined map with a minimal amount of data. We recognize landmarks using a deep neural network, followed with a geometric feature extraction process which promotes the measurement accuracy. The vehicle location and posture are estimated by minimizing a self-defined re-projection residual error to evaluate the map-to-image registration, together with a robust association method. We validate the effectiveness of our approach by applying this method to localize a vehicle in an open dataset, achieving the RMS accuracy of 0.345 meter with reduced sensor setup and map storage compared to the state of art approaches. We also evaluate some key steps and discuss the contribution of the subsystems.
