JointLoc: A Real-time Visual Localization Framework for Planetary UAVs Based on Joint Relative and Absolute Pose Estimation
CoRR(2024)
Abstract
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.
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