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, pp. 150-157.


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:

   author = {G. Huang and M. Kaess and J.J. Leonard},
   title = {Consistent Sparsification for Graph Optimization},
   booktitle = {European Conference on Mobile Robots, ECMR},
   pages = {150-157},
   address = {Barcelona, Spain},
   month = sep,
   year = {2013}
Last updated: August 17, 2017