Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments

TL;DR

This paper presents a vision-based, risk-aware emergency landing system for UAVs in complex urban environments, utilizing semantic segmentation and risk maps to identify safe landing zones amidst moving obstacles.

Contribution

It introduces a novel deep neural network for pixel-level risk assessment and an adaptive algorithm for identifying stable safe landing zones in dynamic urban scenes.

Findings

Achieved over 90% landing success rate in challenging urban scenarios.

Demonstrated robustness against moving obstacles and changing illumination conditions.

Improved risk metrics compared to baseline methods.

Abstract

Landing safely in crowded urban environments remains an essential yet challenging endeavor for Unmanned Aerial Vehicles (UAVs), especially in emergency situations. In this work, we propose a risk-aware approach that harnesses semantic segmentation to continuously evaluate potential hazards in the drone's field of view. By using a specialized deep neural network to assign pixel-level risk values and applying an algorithm based on risk maps, our method adaptively identifies a stable Safe Landing Zone (SLZ) despite moving critical obstacles such as vehicles, people, etc., and other visual challenges like shifting illumination. A control system then guides the UAV toward this low-risk region, employing altitude-dependent safety thresholds and temporal landing point stabilization to ensure robust descent trajectories. Experimental validation in diverse urban environments demonstrates the effectiveness of our approach, achieving over 90% landing success rates in very challenging real scenarios, showing significant improvements in various risk metrics. Our findings suggest that risk-oriented vision methods can effectively help reduce the risk of accidents in emergency landing situations, particularly in complex, unstructured, urban scenarios, densely populated with moving risky obstacles, while potentiating the true capabilities of UAVs in complex urban operations.