An Application of Unscented Kalman Filter for Pose and Motion Estimation Based on Monocular Vision

In a pose and motion estimation system from monocular vision, the extended Kalman filter (EKF) is a widely used filtering strategy generally. However, as defects of the EKF in nonlinear estimation, there exists estimated error, which affects the accuracy of the state estimation, when it is adopted in nonlinear estimation. In order to yield the higher accuracy of pose and motion estimation, the unscented Kalman filter (UKF) using the principle that a set of discretely sampled points can be used to parameterize mean and covariance was employed in this paper. Given a sequence of 2D monocular images of an moving object, using line features on the image plane as measured inputs and a dual quaternions to represent the 3D transformation, the indirect measurement solutions of pose and motion estimation from monocular vision is presented based on EKF and UKF with simulated data. Simulation results have shown that the UKF is a superior alternative to the EKF