This paper presents a novel state estimation system for unmanned aerial vehicle landing. A novel vision algorithm that detects a portion of the marker is developed, and this algorithm extends the detectable range of the vision system for any known marker. A vision-aided navigation algorithm is derived within extended Kalman particle filter and Rao–Blackwellized particle filter frameworks in addition to a standard extended Kalman filter framework. These multihypothesis approaches not only deal well with a highly nonlinear and non-Gaussian distribution of the measurement errors of vision but also result in numerically stable filters. The computational costs are reduced compared to a naive implementation of particle filter, and these algorithms run in real time. This system is validated through numerical simulation, image-in-the-loop simulation, and flight tests.

本文提出了一种新型的无人机着陆状态估计系统。开发了一种检测标记物一部分的新颖视觉算法，该算法扩展了视觉系统对于任何已知标记物的可检测范围。除了标准的扩展卡尔曼滤波器框架之外，视觉辅助导航算法还来自扩展的卡尔曼粒子滤波器和Rao-Blackwellized粒子滤波器框架。这些多假设方法不仅可以很好地处理视觉测量误差的高度非线性和非高斯分布，而且还可以产生数值稳定的滤波器。与天真的粒子过滤器实现相比，计算成本降低了，并且这些算法实时运行。该系统通过数值模拟，图像在环模拟，