Robust Visual-Inertial Navigation for Reliable Guidance in Dynamic Contexts

Urban Air Mobility (UAM) is poised to transform transportation within densely populated cities by leveraging the third dimension—airspace. Vertical Take-Off and Landing (VTOL) and hybrid VTOL-cruise air vehicles are the most compelling and thus popular configurations for UAM applications due to their operational versatility. However, path planning and guidance in such highly dynamic and GNSS-denied environments are likely to produce suboptimal path and erratic maneuvering commands, due to unreliable positioning and attitude determination. To address this challenge, we propose a robust monocular Visual-Inertial Navigation System (VINS), built upon VINS-Mono. Our approach models the uncertainty in both feature tracking and pose estimation, incorporating this into a Mahalanobis-distance-based outlier rejection strategy using a chi-square test. This enables effective suppression of dynamic features, thereby enhancing the robustness of VTOL and hybrid air vehicles navigation and guidance in UAM scenarios. The verification of proposed approach using public datasets demonstrates that consistently high accuracy and robustness can be attained, even under challenging motion profiles.