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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Statistical Pose Averaging with Non-isotropic and Incomplete Relative Measurements
verfasst von : Roberto Tron, Kostas Daniilidis
Erschienen in: Computer Vision – ECCV 2014
Verlag: Springer International Publishing
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In the last few years there has been a growing interest in optimization methods for averaging pose measurements between a set of cameras or objects (obtained, for instance, using epipolar geometry or pose estimation). Alas, existing approaches do not take into consideration that measurements might have different uncertainties (i.e., the noise might not be isotropically distributed), or that they might be incomplete (e.g., they might be known only up to a rotation around a fixed axis). We propose a Riemannian optimization framework which addresses these cases by using covariance matrices, and test it on synthetic and real data.