Optimal Navigation and Obstacle Avoidance for UAV in Urban Canyons

In urban canyons, the autonomous positioning and obstacle avoidance capabilities of Uncrewed Aerial Vehicles (UAV) are challenged by dense obstructions, causing Non-Line-of-Sight (NLOS) conditions that severely degrade Global Navigation Satellite Systems (GNSS) signals. Current urban UAV operations still rely heavily on manual control and conventional GNSS-Inertial Measurement Unit (IMU) fusion, which lack autonomous obstacle avoidance capabilities and fail to provide precise positioning in GNSS-denied environments. To address these limitations, we propose a novel sensor fusion framework that integrates the Sage-Husa adaptive Kalman Filter (SHKF) with intermittent data from GNSS, IMU, and visual odometry supplied from a Visual-Inertial (VINS)-Fusion algorithm to achieve robust positioning and attitude determination. This enhanced sensor fusion architecture enables a 3DVFH*-based local planner to execute real-time obstacle avoidance and path planning toward the intended destinations. Preliminary verification activities conducted in a high-fidelity Gazebo simulation demonstrate the effective performance of the proposed method in terms of positioning and attitude determination accuracy, while also supporting the optimal reliability and efficiency of the considered obstacle avoidance functionality.