不整地環境におけるUAVのエビデンスに基づく着陸地点選定と視覚による着陸
Evidence-Based Landing Site Selection and Vison-Based Landing for UAVs in Unstructured Environments
UAVの自律着陸を、不確実性下の意思決定と視覚サーボによる実行に分離し、フレーム毎の視覚的手がかりから着陸安全性を確率的に推定して最適地点を選定し、ORB特徴追跡とIBVSで精密着陸する手法を提案。実機実験とIsaac Simで検証した。
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著者: Sina Sajjadi, Jacopo Panerati, Sina Soleymanpour, Varunkumar Mehta, Farrokh Janabi-Sharifi, Iraj Mantegh
分類: cs.RO
原文アブストラクト
Autonomous landing in cluttered or unstructured environments remains a safety-critical challenge for unmanned aerial vehicles (UAVs), particularly under noisy perception caused by sensor uncertainty and platform-induced disturbances such as vibration. This paper presents an evidence-based probabilistic framework for autonomous UAV landing that explicitly separates decision-making under uncertainty from execution via visual servoing. Landing safety is modeled as a latent variable and inferred through recursive accumulation of frame-wise visual likelihoods derived from flatness, slope, and obstacle cues, yielding a temporally consistent belief map that is robust to transient perception errors. Physical feasibility is enforced through a hard geometric constraint based on the minimum required landing radius of the UAV, ensuring that undersized but visually appealing regions are rejected. The final landing site is selected using constrained maximum a posteriori estimation. Once selected, the UAV locks onto the target region using ORB feature tracking and performs precise alignment and descent via image-based visual servoing (IBVS). The proposed approach is validated through both real-world laboratory experiments and high-fidelity simulations in Nvidia Isaac Sim, demonstrating consistent, cautious, and stable landing behavior across domains.
