Full- and Reduced-Order Observers for Image-Based Depth Estimation Using Concurrent Learning

Abstract

In this brief, 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, state observers are 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 are used in the CL term to design the full- and 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 the aforementioned metrics on a 7-DoF manipulator with an eye-in-hand configuration.