JointLoc: A Real-time Visual Localization Framework for Planetary UAVs Based on Joint Relative and Absolute Pose Estimation
<|reference_start|>JointLoc: A Real-time Visual Localization Framework for Planetary UAVs Based on Joint Relative and Absolute Pose Estimation: 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.<|reference_end|>
@article{luo2024jointloc:,
    title={JointLoc: A Real-time Visual Localization Framework for Planetary UAVs
  Based on Joint Relative and Absolute Pose Estimation},
    author={Xubo Luo, Xue Wan, Yixing Gao, Yaolin Tian, Wei Zhang and Leizheng Shu},
    journal={arXiv preprint arXiv:2405.07429},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07429},
    primaryClass={cs.RO}
}