基于证据的着陆点选择与基于视觉的着陆：无人机在无结构环境中的应用
Evidence-Based Landing Site Selection and Vison-Based Landing for UAVs in Unstructured Environments
AI总结：
本文提出了一种基于证据的概率框架，用于无人机在复杂环境中自主着陆，通过视觉伺服实现决策与执行分离，确保着陆安全性和物理可行性。
摘要：
自主着陆在杂乱或无结构环境中仍然是无人机的安全关键挑战，特别是在传感器不确定性和平台引起的扰动导致感知噪声的情况下。本文提出了一种基于证据的概率框架，用于自主无人机着陆，明确将不确定性下的决策与通过视觉伺服执行分开。着陆安全性被建模为隐变量，并通过递归累积从平坦度、斜率和障碍物提示中得到的帧级视觉似然性进行推断，从而获得时间一致的信念图，对瞬时感知误差具有鲁棒性。通过基于无人机最小所需着陆半径的硬几何约束来确保物理可行性，确保即使视觉上吸引人的但尺寸不足的区域被拒绝。最终着陆点通过约束最大后验估计选择。一旦选定，无人机使用ORB特征跟踪锁定目标区域，并通过图像基于的视觉伺服（IBVS）进行精确对齐和下降。所提出的方法通过真实世界实验室实验和Nvidia Isaac Sim中的高保真模拟验证，展示了在不同领域中一致、谨慎和稳定的着陆行为。
英文摘要：
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.
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