Real-Time Feature Depth Estimation for Image-Based Visual ServOing

Abstract

Without the 3-D geometry of the target and robust to camera calibration error, image-based visual servoing schemes have gained a lot of attention. However, the depth of the selected feature, which is involved in the interaction matrix relating the time variation of the feature to the velocity twist of the camera, must be estimated correctly to guarantee the stability of the controller. To this end, this paper proposes a new nonlinear reduced-order observer structure to recover the feature depth in real time. Compared with the existing works, the proposed observer has a global asymptotic convergence property and fast convergence rate, and the convergence rate can be easily adjusted only using a single gain parameter. In addition, the proposed observer has a less restrictive observability condition and stronger robustness to noisy measurements. Extensive comparative numerical simulations are carried out to validate the effectiveness of the proposed depth observer.