Probabilistic Analysis of Incremental Light Bundle Adjustment
Author(s)
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
This paper presents a probabilistic analysis of the recently introduced incremental light bundle adjustment
method (iLBA) [6]. In iLBA, the observed 3D points are algebraically eliminated, resulting in a cost function with only
the camera poses as variables, and an incremental smoothing technique is applied for efficiently processing incoming images. While we have already showed that compared to
conventional bundle adjustment (BA), iLBA yields a significant improvement in computational complexity with similar
levels of accuracy, the probabilistic properties of iLBA have
not been analyzed thus far. In this paper we consider the
probability 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. The
latter can be exactly calculated in bundle adjustment (BA)
by 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 and
the true probability distributions are very similar in practice.
Sponsor
Date
2013-01
Extent
Resource Type
Text
Resource Subtype
Proceedings