Deep learning based cross-view image matching for UAV geo-localization.
Xubo Luo, Yaolin Tian, Xue Wan, Jinzhong Xu, Tao Ke

• Due to the large scale and illumination difference between aerial and satellite images, it is challenging that most existing cross-view image matching algorithms fail to localize the UAV robustly and accurately. To solve the above problem, a novel UAV localization framework containing three-stage coarse-to-fine image matching is proposed.
• Paper Project

JointLoc: A Real-time Visual Localization Framework for Planetary UAVs Based on Joint Relative and Absolute Pose Estimation. (Under Review)
Xubo Luo, Xue Wan, Yixing Gao, Yaolin Tian, Wei Zhang, Leizheng Shu

• 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.
• Paper Project

HBANet: A hybrid boundary-aware attention network for infrared and visible image fusion. (Under Review)
Xubo Luo, Liping Wang, Jinshuo Zhang, Dongmei Niu

• In this article, we propose a novel image fusion approach based on hybrid boundary-aware attention, termed HBANet, which models global dependencies across the image and leverages boundary-wise prior knowledge to supplement local details. Specifically, we design a novel mixed boundary-aware attention module that is capable of leveraging spatial information to the fullest extent and integrating long dependencies across different domains.
• Paper Project