IVU-AutoNav: Integrated Visual and UWB Framework for Autonomous Navigation - Shuhui Bu; Jie Zhang; Xiaohan Li; Kun Li; Boni Hu (Drones)
To address the inherent scale ambiguity and positioning drift in monocular visual Simultaneous Localization and Mapping (SLAM), this paper proposes a novel localization method that integrates monocular visual SLAM with Ultra-Wideband (UWB) ranging information. This method enables high-precision localization for unmanned aerial vehicles (UAVs) in complex environments without global navigation information. The proposed framework, IVU-AutoNav, relies solely on distance measurements between a fixed UWB anchor and the UAV’s UWB device. Initially, it jointly solves for the position of the UWB anchor and the scale factor of the SLAM system using the scale-ambiguous SLAM data and ranging information. Subsequently, a pose optimization equation is formulated, which integrates visual reprojection errors and ranging errors, to achieve precise localization with a metric scale. Furthermore, a global optimization process is applied to enhance the global consistency of the localization map and optimize the positions of the UWB anchors and scale factor. The proposed approach is validated through both simulation and experimental studies, demonstrating its effectiveness. Experimental results show a scale error of less than 1.8% and a root mean square error of 0.23 m, outperforming existing state-of-the-art visual SLAM systems. These findings underscore the potential and efficacy of the monocular visual-UWB coupled SLAM method in advancing UAV navigation and localization capabilities.