Feature-matching and extended Kalman filter for stereo ego-motion estimation

Vision-based ego-motion estimation is a widely used method for identifying movements and poses of robots or equipped vehicles utilizing one or more attached cameras. This paper proposes a feature-matching-based method for estimating ego-motion using a calibrated two-camera stereo system. Detected features are separated into two candidate sets. Distant features are selected to provide information, about the rotational components of movements whereas features at closer distance are used to estimate translational components. An extended Kalman filter is used to eliminate the white noise, in order to get a better prediction of both positional and rotational estimations. The method aims to minimise both projection (3D) errors and flow (2D) errors, to ensure a good pair of translation and rotation measures frame by frame. Experiments are carried out for trajectory estimation, and for projection and flow error evaluation.