Causal Camera Motion Estimation by Condensation and Robust Statistics Distance Measures

The problem of Simultaneous Localization And Mapping (SLAM) originally arose from the robotics community and is closely related to the problems of camera motion estimation and structure recovery in computer vision. Recent work in the vision community addressed the SLAM problem using either active stereo or a single passive camera. The precision of camera based SLAM was tested in indoor static environments. However the extended Kalman filters (EKF) as used in these tests are highly sensitive to outliers. For example, even a single mismatch of some feature point could lead to catastrophic collapse in both motion and structure estimates. In this paper we employ a robust-statistics-based condensation approach to the camera motion estimation problem. The condensation framework maintains multiple motion hypotheses when ambiguities exist. Employing robust distance functions in the condensation measurement stage enables the algorithm to discard a considerable fraction of outliers in the data. The experimental results demonstrate the accuracy and robustness of the proposed method.