Robust solution to three-dimensional pose estimation using composite extended Kalman observer and Kalman filter

Abstract

Three-dimensional (3D) pose estimation of a rigid object by only one camera has a vital role in visual servoing systems, and extended Kalman filter (EKF) is vastly used for this task in an unstructured environment. In this study, the stability of the EKF-based 3D pose estimators is analysed in detail. The most challenging issue of the state-of-the-art EKF-based 3D pose estimators is the possibility of its divergence because of the measurement and model noises. By analysing the stability of conventional EKF-based pose estimators a composite technique is proposed to guarantee the stability of the procedure. In the proposed technique, the non-linear-uncertain estimation problem is decomposed into a non-linear-certain observation in addition to a linear-uncertain estimation problem. The first part is handled using the extended Kalman observer and the second part is accomplished by a simple Kalman filter. Finally, some experimental and simulation results are given in order to verify the robustness of the method and compare the performance of the proposed method in noisy and uncertain environment to the conventional techniques.