An Introduction to Inertial and Visual Sensing
International Journal of Robotics Research
Object Tracking from Unstabilized Platforms by Particle Filtering with Embedded Camera Ego Motion
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Robust tracking in aerial imagery based on an ego-motion Bayesian model
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Iterative estimation of 3d transformations for object alignment
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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We present a probabilistic framework for correspondence and egomotion. First, we suggest computing probability distributions of correspondence. This has the advantage of being robust to points subject to the aperture effect and repetitive structure, while giving up no information at feature points. Additionally, correspondence probability distributions can be computed for every point in the scene. Next, we generate a probability distribution over the motions, from these correspondence probability distributions, through a probabilistic notion of the epipolar constraint. Finding the maximum in this distribution is shown to be a generalization of least-squared epipolar minimization. We will show that because our technique allows so much correspondence information to be extracted, more accurate egomotion estimation is possible.