Title: Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes,
Abstract: Full autonomy for fixed-wing unmanned aerial vehicles (UAVs) requires the
capability to autonomously detect potential landing sites in unknown and
unstructured terrain, allowing for self-governed mission completion or handling
of emergency situations. In this work, we propose a perception system
addressing this challenge by detecting landing sites based on their texture and
geometric shape without using any prior knowledge about the environment. The
proposed method considers hazards within the landing region such as terrain
roughness and slope, surrounding obstacles that obscure the landing approach
path, and the local wind field that is estimated by the on-board EKF. The
latter enables applicability of the proposed method on small-scale autonomous
planes without landing gear. A safe approach path is computed based on the UAV
dynamics, expected state estimation and actuator uncertainty, and the on-board
computed elevation map. The proposed framework has been successfully tested on
photo-realistic synthetic datasets and in challenging real-world environments.