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Carnegie Mellon Robotics Institute

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Abstract

Stereo visual odometry and dense scene reconstruction depend critically on accurate calibration of the extrinsic (relative) stereo camera poses. We present an algorithm for continuous, online stereo extrinsic re-calibration operating only on sparse stereo correspondences on a per-frame basis. We obtain the 5 degree of freedom extrinsic pose for each frame, with a fixed baseline, making it possible to model time-dependent variations. The initial extrinsic estimates are found by minimizing epipolar errors, and are refined via a Kalman Filter (KF). Observation covariances are derived from the Cr ́ mer-Rao lower bound of the solution uncertainty. The algorithm operates at frame rate with unoptimized Matlab code with over 1000 correspondences per frame. We validate its performance using a variety of real stereo datasets and simulations.

Keywords

recalibration, stereo, stereo extrinsics

Notes

Sponsor: Qatar National Research Fund

Associated Center(s) / Consortia: National Robotics Engineering Center

Associated Project(s): LNG Pipe Vision

Text Reference

Peter Hansen, Hatem Said Alismail, Peter Rander, and Brett Browning, "Online Continuous Stereo Extrinsic Parameter Estimation ," International conference on Computer Vision and Pattern Recognition, June, 2012.

BibTeX Reference

@inproceedings{Alismail_2012_7027,
   author = "Peter Hansen and Hatem Said Alismail and Peter Rander and Brett Browning",
   title = "Online Continuous Stereo Extrinsic Parameter Estimation ",
   booktitle = "International conference on Computer Vision and Pattern Recognition",
   month = "June",
   year = "2012",
}

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