Robust map generation for fixed-wing UAVs with low-cost highly-oblique monocular cameras

Abstract

Accurate and robust real-time map generation onboard of a fixed-wing UAV is essential for obstacle avoidance, path planning, and critical maneuvers such as autonomous take-off and landing. Due to the computational constraints, the required robustness and reliability, it remains a challenge to deploy a fixed-wing UAV with an online-capable, accurate and robust map generation framework. While photogrammetric approaches have underlying assumptions on the structure and the view of the camera, generic simultaneous localization and mapping (SLAM) approaches are computationally demanding. This paper presents a framework that uses the autopilot's state estimate as a prior for sliding window bundle adjustment and map generation. Our approach outputs an accurate geo-referenced dense point-cloud which was validated in simulation on a synthetic dataset and on two real-world scenarios based on ground control points.