Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentation

TL;DR

This paper presents a real-time, vision-based risk assessment method for autonomous UAV landings in urban environments, utilizing deep neural networks for semantic segmentation to identify safe landing zones during emergencies.

Contribution

It introduces a novel approach combining SegFormer-based semantic segmentation with risk mapping for autonomous urban UAV landings, especially in emergency scenarios.

Findings

Effective real-time segmentation of urban environments.

Successful identification of safe landing zones.

Potential to enhance UAV safety in urban civil applications.

Abstract

In this paper, we address the vision-based autonomous landing problem in complex urban environments using deep neural networks for semantic segmentation and risk assessment. We propose employing the SegFormer, a state-of-the-art visual transformer network, for the semantic segmentation of complex, unstructured urban environments. This approach yields valuable information that can be utilized in smart autonomous landing missions, particularly in emergency landing scenarios resulting from system failures or human errors. The assessment is done in real-time flight, when images of an RGB camera at the Unmanned Aerial Vehicle (UAV) are segmented with the SegFormer into the most common classes found in urban environments. These classes are then mapped into a level of risk, considering in general, potential material damage, damaging the drone itself and endanger people. The proposed strategy is validated through several case studies, demonstrating the huge potential of semantic segmentation-based strategies to determining the safest landing areas for autonomous emergency landing, which we believe will help unleash the full potential of UAVs on civil applications within urban areas.