Consistent Sparsification for Graph Optimization
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“Consistent Sparsification for Graph Optimization” by G. Huang, M. Kaess, and J.J. Leonard. In European Conference on Mobile Robots (ECMR), (Barcelona, Spain), Sep. 2013.
Abstract
In a standard pose-graph formulation of simultaneous localization and mapping (SLAM), due to the continuously increasing numbers of nodes (states) and edges (measurements), the graph may grow prohibitively too large for long-term navigation. This motivates us to systematically reduce the pose graph amenable to available processing and memory resources. In particular, in this paper we introduce a consistent graph sparsification scheme: i) sparsifying nodes via marginalization of old nodes, while retaining all the information (consistent relative constraints) - which is conveyed in the discarded measurements - about the remaining nodes after marginalization; and ii) sparsifying edges by formulating and solving a consistent l1-regularized minimization problem, which automatically promotes the sparsity of the graph. The proposed approach is validated on both synthetic and real data.
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BibTeX entry:
@inproceedings{Huang13ecmr2,
author = {G. Huang and M. Kaess and J.J. Leonard},
title = {Consistent Sparsification for Graph Optimization},
booktitle = {European Conference on Mobile Robots (ECMR)},
address = {Barcelona, Spain},
month = {Sep},
year = {2013}
}