This paper studies the problem of designing a certified vision-based state estimator for autonomous landing systems. In such a system, a neural network (NN) processes images from a camera to estimate the aircraft relative position with respect to the runway. We propose an algorithm to design such NNs with certified properties in terms of their ability to detect runways and provide accurate state estimation. At the heart of our approach is the use of geometric models of perspective cameras to obtain a mathematical model that captures the relation between the aircraft states and the inputs. We show that such geometric models enjoy mixed monotonicity properties that can be used to design state estimators with certifiable error bounds. We show the effectiveness of the proposed approach using an experimental testbed on data collected from event-based cameras.

翻译：本文研究了为自主降落系统设计可认证视觉状态估计器的问题。在该系统中，神经网络处理来自摄像机的图像，以估计飞行器相对于跑道的相对位置。我们提出了一种算法，用于设计具有可认证特性的神经网络，这些特性体现在其检测跑道并提供精确状态估计的能力上。我们方法的核心是利用透视摄像机的几何模型，获得一个捕捉飞行器状态与输入之间关系的数学模型。结果表明，此类几何模型具有混合单调性特性，可用于设计具有可证明误差界的状态估计器。我们通过基于事件相机采集数据的实验测试平台，展示了所提方法的有效性。