发表机构
Tennessee Technological University（田纳西理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对GPS拒止环境下视觉无人机着陆，提出贝叶斯数据增强框架集成高保真模拟器，迭代重训练停机坪检测DNN，以最大化着陆性能为目标，实验验证了改进的着陆性能和更紧的置信区间。
AI 中文摘要
在GPS拒止或杂乱的城市环境中，基于视觉的着陆对于可靠的无人机任务至关重要。现实世界的着陆点通常是非结构化的且高度多变，要求感知系统具备强大的泛化能力。使用合成数据增强训练的深度神经网络（DNN）为学习不同车辆和环境状态下的着陆点特征提供了可扩展的解决方案。然而，计算成本高昂的DNN重训练以及通过试飞进行具有挑战性的性能验证，限制了详尽的模型微调，并需要优化的重训练流程。在这项工作中，我们部署了一个贝叶斯数据增强框架，该框架与具有高保真车辆动力学特性的逼真模拟器集成，以迭代地重训练直升机停机坪检测器DNN，将最大化着陆性能作为目标函数。我们在不同环境条件和车辆状态下的逼真模拟器中进行了实验来验证我们的框架，展示了改进的着陆性能以及对预测着陆结果更紧的置信区间。
英文摘要
In GPS-denied or cluttered urban environments, vision-based landing is essential for reliable UAV missions. Real-world landing sites are often unstructured and highly variable, requiring strong generalization by the perception system. Deep Neural Networks (DNNs) trained with synthetic data augmentation offer a scalable solution for learning landing-site features across diverse vehicle and environmental states. However, computationally expensive DNN retraining, along with challenging performance validation via test flights, limits exhaustive model fine-tuning and necessitates an optimized retraining pipeline. In this work, we deploy a Bayesian data augmentation framework integrated with a photorealistic simulator featuring high-fidelity vehicle dynamics to iteratively retrain the helipad detector DNN, maximizing landing performance as the objective function. We validate our framework with experiments in a photorealistic simulator under different environmental conditions and vehicle states, demonstrating improved landing performance and tighter confidence intervals on predicted landing outcomes.
