Factor descent optimization for sparsification in graph SLAM

Trabajo presentado a la European Conference on Mobile Robots (ECMR), celebrada en Paris (Francia) del 6 al 8 de septiembre de 2017.In the context of graph-based simultaneous localization and mapping, node pruning consists in removing a subset of nodes from the graph, while keeping the graph¿s information content as close as possible to the original. One often tackles this problem locally by isolating the Markov blanket sub-graph of a node, marginalizing this node and sparsifying the dense result. It means computing an approximation with a new set of factors. For a given approximation topology, the factors¿ mean and covariance that best approximate the original distribution can be obtained through minimization of the Kullback-Liebler divergence. For simple topologies such as Chow-Liu trees, there is a closed form for the optimal solution. However, a tree is oftentimes too sparse to explain some graphs. More complex topologies require nonlinear iterative optimization. In the present paper we propose Factor Descent, a new iterative optimization method to sparsify the dense result of node marginalization, which works by iterating factor by factor. We also provide a thorough comparison of our approach with state-of-the-art methods in real world datasets with regards to the obtained solution and convergence rates.This work has been supported by the Spanish Ministry of Economy and Competitiveness under Project ROBINSTRUCT (TIN2014-58178-R), by the EU H2020 Project LOGIMATIC (H2020-Galileo-2015-1-687534) and by the María de Maeztu Seal of Excellence to IRI MDM-2016-0656.Peer Reviewe