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.
@article{arxiv.2401.09658,
title = {An adaptive optimal control approach to monocular depth observability maximization},
author = {Tochukwu Elijah Ogri and Muzaffar Qureshi and Zachary I. Bell and Kristy Waters and Rushikesh Kamalapurkar},
journal= {arXiv preprint arXiv:2401.09658},
year = {2024}
}