基于语义分割的城市环境自主着陆风险评估
Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentation
AI总结：
针对城市复杂环境下无人机自主着陆风险评估问题，提出采用SegFormer视觉Transformer网络进行实时语义分割并映射风险等级，经案例验证可有效确定安全着陆区，助力无人机城市民用应用发展。
摘要：
本文针对复杂城市环境下基于视觉的自主着陆问题，采用深度神经网络进行语义分割与风险评估。我们提出采用SegFormer——一种先进的视觉Transformer网络，对复杂非结构化城市环境进行语义分割。该方法可提供有价值的信息，用于智能自主着陆任务，尤其是系统故障或人为失误导致的紧急着陆场景。评估在实时飞行中开展：无人机（UAV）搭载的RGB相机采集的图像经SegFormer分割为城市环境中最常见的类别。随后将这些类别映射为风险等级，综合考量潜在财产损失、无人机自身损坏及人员伤亡风险。通过多个案例研究验证了所提策略，证明基于语义分割的策略在确定自主紧急着陆最安全着陆区域方面具有巨大潜力，我们认为这将有助于释放无人机在城市民用应用中的全部潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
