An adaptive optimal control approach to monocular depth observability maximization
CoRR(2024)
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.
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