An accurate estimation of 3-D position and orientation of a moving object for robot stereo vision: Kalman filter approach

A Kalman filter approach for accurately estimating the 3-D position and orientation of a moving object with respect to the robot base frame is proposed. This approach significantly differs from other approaches in that motion is estimated with the uncertainty of the position of a camera taken into account. Emphasis is also given to finding a solution to the following problem of motion estimation using a long sequence of images: the images taken from a longer distance suffer from a larger noise-to-signal ratio, which results in larger errors in 3-D reconstruction and, thereby, causes a series degradation in motion estimation. To solve this problem, the authors have derived a set of discrete Kalman filter equations for motion estimation. The measurement equation is obtained by analyzing the effect of white Gaussian noise in 2-D images on 3-D positional errors, and the system dynamic equation is formulated in terms of the measurement noise in 2-D images. Simulation results indicate that the Kalman filter equations derived present an accurate model for the estimation of 3-D position and orientation, thus providing significant error reduction in the presence of large measurement noise in a long sequence of images, as well as allowing a shorter transition period for convergence to the true values