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Consistent Sparsification for Graph Optimization

Guoquan Huang, Michael Kaess and John J. Leonard
Conference Paper, Proceedings of European Conference on Mobile Robots (ECMR), September, 2013

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In a standard pose-graph formulation of simul- taneous localization and mapping (SLAM), due to the con- tinuously 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.

author = {Guoquan Huang and Michael Kaess and John J. Leonard},
title = {Consistent Sparsification for Graph Optimization},
booktitle = {Proceedings of European Conference on Mobile Robots (ECMR)},
year = {2013},
month = {September},
} 2017-09-13T10:39:16-04:00