Efficient, causal camera tracking in unprepared environments ?
This paper addresses the problem of tracking the 3D pose of a camera in space, using the images it acquires while moving freely in unmodeled, arbitrary environments. A novel feature-based approach for camera tracking is proposed, intended to facilitate tracking in on-line, time-critical applications such as video see-through augmented reality. In contrast to several existing methods which are designed to operate in a batch, off-line mode, assuming that the whole video sequence to be tracked is available before tracking commences, the proposed method operates on images incrementally. At its core lies a feature-based 3D plane tracking technique, which permits the estimation of the homographies induced by a virtual 3D plane between successive image pairs. Knowledge of these homographies allows the corresponding projection matrices encoding camera motion to be expressed in a common projective frame and, therefore, to be recovered directly, without estimating 3D structure. Projective camera matrices are then upgraded to Euclidean and used for recovering structure, which is in turn employed for refining the projection matrices through local resectioning. The proposed approach is causal, is tolerant to erroneous and missing feature matches, does not require modifications of the environment and has computational requirements that permit a near real-time implementation. Extensive experimental results demonstrating the performance of the approach on several image sequences are included.
camera tracking augmented reality plane tracking egomotion estimation 3d structure and motion estimation corner matching matchmoving