Kalman and Smooth Variable Structure Filters for Pose Estimation in Robotic Visual Servoing

Abstract

Pose estimation problem of an object in real time is an important issue for robotic visual servoing (RVS). Many pose estimation schemes in RVS rely on an extended Kalman filter (EKF) that provides good accurate estimation. However, it may cause the estimation to become unstable because of nonlinear modeling uncertainties. While, smooth variable structure filter (SVSF) is a new method that is more robust to disturbances and uncertainties. In this paper, a novel pose estimate method is developed based on the EKF and SVSF. It is combined the accuracy of the EKF and the robustness provided by the SVSF that will lead to more accurate state estimates and improve robustness to modeling errors and uncertainties. The resulting algorithms are called the EKF-SVSF. The simulation results are provided to demonstrate the robustness and accuracy.