Title: Vision-based Autonomous Landing in Catastrophe-Struck Environments,
Abstract: Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving
technology that can enable identification of survivors under collapsed
buildings in the aftermath of natural disasters such as earthquakes or gas
explosions. However, these UAVs have to be able to autonomously land on debris
piles in order to accurately locate the survivors. This problem is extremely
challenging as the structure of these debris piles is often unknown and no
prior knowledge can be leveraged. In this work, we propose a computationally
efficient system that is able to reliably identify safe landing sites and
autonomously perform the landing maneuver. Specifically, our algorithm computes
costmaps based on several hazard factors including terrain flatness, steepness,
depth accuracy and energy consumption information. We first estimate dense
candidate landing sites from the resulting costmap and then employ clustering
to group neighboring sites into a safe landing region. Finally, a minimum-jerk
trajectory is computed for landing considering the surrounding obstacles and
the UAV dynamics. We demonstrate the efficacy of our system using experiments
from a city scale hyperrealistic simulation environment and in real-world
scenarios with collapsed buildings.