用视觉和姿态信息实时规划低空飞行路径，提升避障效率
Terrain-aware Low Altitude Path Planning
- 结合行为克隆与自监督学习训练飞行策略
- 路径平均高度降低24.7%，优于传统方法
- 适合无人机实时低空自主导航场景
本文研究仅使用机载相机的RGB图像和飞行器姿态信息，在实时条件下生成贴近地形（NOE）飞行的低空路径规划。提出一种新颖的训练方法，融合行为克隆与自监督学习，使学习到的策略能够对专家规划器生成的路径进行优化。仿真结果显示，相比标准行为克隆方法，路径平均高度降低24.7%。
原文摘要 · Abstract (English)
In this paper, we study the problem of generating low-altitude path plans for nap-of-the-earth (NOE) flight in real time with only RGB images from onboard cameras and the vehicle pose. We propose a novel training method that combines behavior cloning and self-supervised learning, where the self-supervision component allows the learned policy to refine the paths generated by the expert planner. Simulation studies show 24.7% reduction in average path elevation compared to the standard behavior cloning approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。