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Preemptive RANSAC for live structure and motion estimation

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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.

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Author notes

  1. David Nistér

    Present address: Center for Visualization and Virtual Environments, Computer Science Department, University of Kentucky, 1 Quality Street, Suite 800, Lexington, KY, 40507-1464, USA

Authors and Affiliations

  1. Sarnoff Corporation, CN5300, Princeton, NJ, 08530, USA

    David Nistér

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  1. David Nistér
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Correspondence to David Nistér.

Additional information

Prepared through collaborative participation in the Robotics Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0012. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon.

David Nistér received PhD degree in computer vision, numerical analysis and computing science from the Royal Institute of Technology (KTH), Stockholm, Sweden, with the thesis ‘Automatic Dense Reconstruction from Uncalibrated Video Sequences’. He is currently an assistant professor at the Computer Science Department and the Center for Visualization and Virtual Environments, University of Kentucky, Lexington. Before joining UK, he was a researcher in the Vision Technologies Laboratory, Sarnoff Corporation, Princeton, and Visual Technology, Ericsson Research, Stockholm, Sweden. His research interests include computer vision, computer graphics, structure from motion, multiple view geometry, Bayesian formulations, tracking, recognition, image and video compression. He is a member of the IEEE and American Mensa.

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Nistér, D. Preemptive RANSAC for live structure and motion estimation. Machine Vision and Applications 16, 321–329 (2005). https://doi.org/10.1007/s00138-005-0006-y

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