Accurate and robust ego-motion estimation using expectation maximization

A novel robust visual-odometry technique, called EM-SE(3) is presented and compared against using the random sample consensus (RANSAC) for ego-motion estimation. In this contribution, stereo-vision is used to generate a number of minimal-set motion hypothesis. By using EM-SE(3), which involves expectation maximization on a local linearization of the rigid-body motion group SE(3), a distinction can be made between inlier and outlier motion hypothesis. At the same time a robust mean motion as well as its associated uncertainty can be computed on the selected inlier motion hypothesis. The data-sets used for evaluation consist of synthetic and large real-world urban scenes, including several independently moving objects. Using these data-sets, it will be shown that EM-SE(3) is both more accurate and more efficient than RANSAC.