  In this paper concurrent learning (CL)-based full and reduced order observers
for a perspective dynamical system (PDS) are developed. The PDS is a widely
used model for estimating the depth of a feature point from a sequence of
camera images. Building on the current progress of CL for parameter estimation
in adaptive control, a state observer is developed for the PDS model where the
inverse depth appears as a time-varying parameter in the dynamics. The data
recorded over a sliding time window in the near past is used in the CL term to
design the full and the reduced order state observers. A Lyapunov-based
stability analysis is carried out to prove the uniformly ultimately bounded
(UUB) stability of the developed observers. Simulation results are presented to
validate the accuracy and convergence of the developed observers in terms of
convergence time, root mean square error (RMSE) and mean absolute percentage
error (MAPE) metrics. Real world depth estimation experiments are performed to
demonstrate the performance of the observers using aforementioned metrics on a
7-DoF manipulator with an eye-in-hand configuration.
