Vision-based camera motion recovery for augmented reality

We address the problem of tracking the 3D position and orientation of a camera, using the images it acquires while moving freely in unmodeled, arbitrary environments. This task has a broad spectrum of useful applications in domains such as augmented reality and video post production. Most of the existing methods for vision-based camera tracking are designed to operate in a batch, off-line mode, assuming that the whole video sequence to be tracked is available before tracking commences. Typically, such methods operate noncausally, processing video frames backwards and forwards in time as they see fit. Furthermore, they resort to optimization in very high dimensional spaces, a process that is computationally intensive. For these reasons, batch methods are inapplicable to tracking in online, time-critical applications such as video see-through augmented reality. This paper puts forward a novel feature-based approach for camera tracking. The proposed approach operates on images continuously as they are acquired, has realistic computational requirements and does not require modifications of the environment. Sample experimental results demonstrating the feasibility of the approach on video images are also provided