Statistical Pose Averaging with Non-isotropic and Incomplete Relative Measurements
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Abstract
In the last few years there has been a growing interest in optimization methods for averaging pose measurements between a set of cameras or ob jects (obtained, for instance, using epipolar geometry or pose estimation). Alas, existing approaches do not take into considera- tion 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.
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