Probabilistic Analysis of Incremental Light Bundle AdjustmentReport as inadecuate

Probabilistic Analysis of Incremental Light Bundle Adjustment

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This paper presents a probabilistic analysis of the recently introduced incremental light bundle adjustmentmethod iLBA 6. In iLBA, the observed 3D points are algebraically eliminated, resulting in a cost function with onlythe camera poses as variables, and an incremental smoothing technique is applied for efficiently processing incoming images. While we have already showed that compared toconventional bundle adjustment BA, iLBA yields a significant improvement in computational complexity with similarlevels of accuracy, the probabilistic properties of iLBA havenot been analyzed thus far. In this paper we consider theprobability distribution that corresponds to the iLBA cost function, and analyze how well it represents the true density of the camera poses given the image measurements. Thelatter can be exactly calculated in bundle adjustment BAby marginalizing out the 3D points from the joint distribution of camera poses and 3D points. We present a theoretical analysis of the differences in the way that LBA and BA use measurement information. Using indoor and outdoor datasets we show that the first two moments of the iLBA andthe true probability distributions are very similar in practice.

Computational Perception and Robotics - Computational Perception and Robotics Publications -

Author: Indelman, Vadim - Roberts, Richard - Dellaert, Frank - -


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