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Abstract or Description

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 Cramer-Rao lower bound of the solu- ´ tion 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.



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Published In

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012, 1059-1066.