Consistent Sparsification for Graph Optimization

TitleConsistent Sparsification for Graph Optimization
Publication TypeConference Paper
Year of Publication2013
AuthorsHuang G, Kaess M, Leonard JJ
Conference NameProc of European Conference on Mobile Robots (ECMR)
Conference LocationBarcelona, Spain

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.