Impact of Landmark Parametrization on Monocular EKF-SLAM with Points and Lines
International Journal of Computer Vision
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Estimation of structure and motion in computer vision systems can be performed using a dynamic systems approach, where states and parameters in a perspective system are estimated. We present a novel on-line method for structure and motion estimation in densely sampled image sequences. The proposed method is based on an extended Kalman filter and a novel parameterization. We assume calibrated cameras and derive a dynamic system describing the motion of the camera and the image formation. By a change of coordinates, we represent this system by normalized image coordinates and the inverse depths. Then we apply an extended Kalman filter for estimation of both structure and motion. The performance of the proposed method is demonstrated in both simulated and real experiments. We furthermore compare our method to the unified inverse depth parameterization and show that we achieve superior results.