Preemptive RANSAC for live structure and motion estimation

Authors:
David Nistér
Affiliations:
Center for Visualization and Virtual Environments, Computer Science Department, University of Kentucky, 1 Quality Street, Suite 800, Lexington, KY, 40507-1464, USA
Venue:
Machine Vision and Applications
Year:
2005

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Abstract

A system capable of performing robust live ego-motion estimation for perspective cameras is presented. The system is powered by random sample consensus with preemptive scoring of the motion hypotheses. A general statement of the problem of efficient preemptive scoring is given. Then a theoretical investigation of preemptive scoring under a simple inlier–outlier model is performed. A practical preemption scheme is proposed and it is shown that the preemption is powerful enough to enable robust live structure and motion estimation.