PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.

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Relative pose estimation for instrumented, calibrated imaging platforms

Summary


Oscar Pizarro, Ryan Eustice and Hanumant Singh, Relative pose estimation for instrumented, calibrated imaging platforms. In Proceedings of Digital Image Computing: Techniques and Applications, pages 601-612, Sydney, Australia, December 2003.

Abstract

Recent efforts in robust estimation of the two-view relation have focused on uncalibrated cameras with no prior knowledge of pose. However, in practice robotic vehicles that perform image-based navigation and mapping typically do carry a calibrated camera and pose sensors; this additional knowledge is currently not being exploited. This paper presents three contributions in using vision with instrumented and calibrated platforms. First, we improve the performace of the correspondence stage by using uncertain measurements from egomotion sensors to constrain possible matches. Second, we assume wide-baseline conditions and propose Zernike moments to describe affine invariant features. Third, we robustly estimate the essential matrix with a new 6-point algorithm. Our solution is simpler than the minimal 5-point one and, unlike the linear 6-point solution, does not fail on planar scenes. While the contributions are general, we present structure and motion results from an underwater robotic survey.

Bibtex entry

@INPROCEEDINGS { opizarro-2003a,
    AUTHOR = { Oscar Pizarro and Ryan Eustice and Hanumant Singh },
    TITLE = { Relative pose estimation for instrumented, calibrated imaging platforms },
    BOOKTITLE = { Proceedings of Digital Image Computing: Techniques and Applications },
    YEAR = { 2003 },
    MONTH = { December },
    ADDRESS = { Sydney, Australia },
    PAGES = { 601--612 },
}