JointLoc: 相対・絶対姿勢推定を統合した惑星探査UAV向けリアルタイム視覚自己位置推定フレームワーク
JointLoc: A Real-time Visual Localization Framework for Planetary UAVs Based on Joint Relative and Absolute Pose Estimation
GNSSが使えない惑星環境で、衛星地図と機載カメラ画像から絶対2自由度姿勢と相対6自由度姿勢を適応的に融合し、UAVの位置をリアルタイム推定する手法を提案。火星地形データセットでRMSE 0.237mを達成した。
著者: Xubo Luo, Xue Wan, Yixing Gao, Yaolin Tian, Wei Zhang, Leizheng Shu
分類: cs.RO
原文アブストラクト
Unmanned aerial vehicles (UAVs) visual localization in planetary aims to estimate the absolute pose of the UAV in the world coordinate system through satellite maps and images captured by on-board cameras. However, since planetary scenes often lack significant landmarks and there are modal differences between satellite maps and UAV images, the accuracy and real-time performance of UAV positioning will be reduced. In order to accurately determine the position of the UAV in a planetary scene in the absence of the global navigation satellite system (GNSS), this paper proposes JointLoc, which estimates the real-time UAV position in the world coordinate system by adaptively fusing the absolute 2-degree-of-freedom (2-DoF) pose and the relative 6-degree-of-freedom (6-DoF) pose. Extensive comparative experiments were conducted on a proposed planetary UAV image cross-modal localization dataset, which contains three types of typical Martian topography generated via a simulation engine as well as real Martian UAV images from the Ingenuity helicopter. JointLoc achieved a root-mean-square error of 0.237m in the trajectories of up to 1,000m, compared to 0.594m and 0.557m for ORB-SLAM2 and ORB-SLAM3 respectively. The source code will be available at https://github.com/LuoXubo/JointLoc.
