A Kalman filter approach for accurate 3D motion estimation from a sequence of stereo images

The authors present a Kalman filter approach for accurately estimating the 3D position and orientation of a moving object from a sequence of stereo images. One of the drawbacks of using a long sequence of images is that the noisy images taken from a longer distance result in larger errors in 3D reconstruction and, consequently, lead to a serious degradation in motion estimation. To overcome this drawback, the authors have derived a set of Kalman filter equations for motion estimation in the quaternion representation. The measurement equation is obtained by analyzing the effect of white Gaussian noise in 2D images on 3D positional errors, and incorporating the optimal 3D reconstruction under the consistency constraint. The state propagation equation is formulated by specifying the error between the true rotation and the nominal rotation in terms of the measurement noise in 2D images. Actual rotation parameters have been computed from the estimated quaternions by the iterated least-squares method. Simulation results indicate that the equations derived are accurate for 3D motion estimation