On the Visual-based Safe Landing of UAVs in Populated Areas: a Crucial Aspect for Urban Deployment

TL;DR

This paper presents a visual-based algorithm for UAVs to identify safe landing zones in crowded areas, enhancing safety during emergency landings in urban environments.

Contribution

A novel deep learning and tracking approach for detecting and monitoring safe landing zones in dynamic, crowded scenarios using UAV-mounted cameras.

Findings

Effective in crowded scenarios with moving people

Successfully tested on public datasets and real-world UAV flights

Promising results in preventing harm during emergency landings

Abstract

Autonomous landing of Unmanned Aerial Vehicles (UAVs) in crowded scenarios is crucial for successful deployment of UAVs in populated areas, particularly in emergency landing situations where the highest priority is to avoid hurting people. In this work, a new visual-based algorithm for identifying Safe Landing Zones (SLZ) in crowded scenarios is proposed, considering a camera mounted on an UAV, where the people in the scene move with unknown dynamics. To do so, a density map is generated for each image frame using a Deep Neural Network, from where a binary occupancy map is obtained aiming to overestimate the people's location for security reasons. Then, the occupancy map is projected to the head's plane, and the SLZ candidates are obtained as circular regions in the head's plane with a minimum security radius. Finally, to keep track of the SLZ candidates, a multiple instance tracking algorithm is implemented using Kalman Filters along with the Hungarian algorithm for data association. Several scenarios were studied to prove the validity of the proposed strategy, including public datasets and real uncontrolled scenarios with people moving in public squares, taken from an UAV in flight. The study showed promising results in the search of preventing the UAV from hurting people during emergency landing.