  The remarkable growth of unmanned aerial vehicles (UAVs) has also sparked
concerns about safety measures during their missions. To advance towards safer
autonomous aerial robots, this work presents a vision-based solution to
ensuring safe autonomous UAV landings with minimal infrastructure. During
docking maneuvers, UAVs pose a hazard to people in the vicinity. In this paper,
we propose the use of a single omnidirectional panoramic camera pointing
upwards from a landing pad to detect and estimate the position of people around
the landing area. The images are processed in real-time in an embedded
computer, which communicates with the onboard computer of approaching UAVs to
transition between landing, hovering or emergency landing states. While
landing, the ground camera also aids in finding an optimal position, which can
be required in case of low-battery or when hovering is no longer possible. We
use a YOLOv7-based object detection model and a XGBooxt model for localizing
nearby people, and the open-source ROS and PX4 frameworks for communication,
interfacing, and control of the UAV. We present both simulation and real-world
indoor experimental results to show the efficiency of our methods.
