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In this paper concurrent learning (CL)-based full and reduced order observersfor a perspective dynamical system (PDS) are developed. The PDS is a widelyused model for estimating the depth of a feature point from a sequence ofcamera images. Building on the current progress of CL for parameter estimationin adaptive control, a state observer is developed for the PDS model where theinverse depth appears as a time-varying parameter in the dynamics. The datarecorded over a sliding time window in the near past is used in the CL term todesign the full and the reduced order state observers. A Lyapunov-basedstability analysis is carried out to prove the uniformly ultimately bounded(UUB) stability of the developed observers. Simulation results are presented tovalidate the accuracy and convergence of the developed observers in terms ofconvergence time, root mean square error (RMSE) and mean absolute percentageerror (MAPE) metrics. Real world depth estimation experiments are performed todemonstrate the performance of the observers using aforementioned metrics on a7-DoF manipulator with an eye-in-hand configuration.

Extension of Full and Reduced Order Observers for Image-based Depth Estimation using Concurrent Learning