一种面向单目深度可观测性最大化的自适应最优控制方法
An adaptive optimal control approach to monocular depth observability maximization
AI总结：
本文提出基于积分并发学习的单目深度观测器，在无状态信息条件下估计特征距离，并通过无限时域最优调节实现相机定位与可观测性最大化，仿真验证了方法的有效性。
摘要：
本文提出一种基于积分并发学习（ICL）的观测器，用于单目相机在状态信息不可用的限制下，准确估计到静止目标上特征的欧氏距离。利用距离估计，求解一个无限时域最优调节问题，旨在将相机调节至目标位置的同时最大化特征可观测性。采用基于李雅普诺夫的稳定性分析，保证深度估计的指数收敛性以及目标位置相对于相机的输入-状态稳定性。所提方法的有效性在仿真中得到验证，并给出了一个说明可观测性提升的表格。
英文摘要：
This paper presents an integral concurrent learning (ICL)-based observer for a monocular camera to accurately estimate the Euclidean distance to features on a stationary object, under the restriction that state information is unavailable. Using distance estimates, an infinite horizon optimal regulation problem is solved, which aims to regulate the camera to a goal location while maximizing feature observability. Lyapunov-based stability analysis is used to guarantee exponential convergence of depth estimates and input-to-state stability of the goal location relative to the camera. The effectiveness of the proposed approach is verified in simulation, and a table illustrating improved observability is provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
