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2007
IEEE

Approximate covariance estimation in graphical approaches to SLAM

9 years 4 months ago
Approximate covariance estimation in graphical approaches to SLAM
— Smoothing and optimization approaches are an effective means for solving the simultaneous localization and mapping (SLAM) problem. Most of the existing techniques focus mainly on determining the most likely map and leave open how to efficiently compute the marginal covariances. These marginal covariances, however, are essential for solving the data association problem. In this paper we present a novel algorithm for computing an approximation of the marginal. In experiments we demonstrate that our approach outperforms two commonly used techniques, namely loopy belief propagation and belief propagation on a spanning tree. Compared to these approaches, our algorithm yields better estimates while preserving the same time complexity.
Gian Diego Tipaldi, Giorgio Grisetti, Wolfram Burg
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IROS
Authors Gian Diego Tipaldi, Giorgio Grisetti, Wolfram Burgard
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