A Provably Consistent Method for Imposing Sparsity in Feature-Based SLAM Information Filters

An open problem in Simultaneous Localization and Mapping (SLAM) is the development of algorithms which scale with the size of the environment. A few promising methods exploit the key insight that representing the posterior in the canonical form parameterized by a sparse information matrix provides significant advantages regarding computational efficiency and storage requirements. Because the information matrix is naturally dense in the case of feature-based SLAM, additional steps are necessary to achieve sparsity. The delicate issue then becomes one of performing this sparsification in a manner which is consistent with the original distribution. In this paper, we present a SLAM algorithm based in the information form in which sparseness is preserved while maintaining consistency. We describe an intuitive approach to controlling the population of the information matrix by essentially ignoring a small fraction of proprioceptive measurements whereby we track a modified version of the...

• Type: Conference
• Year: 2005
• Where: ISRR
• Authors: Matthew Walter, Ryan Eustice, John J. Leonard
• Keywords: Information Matrix, ISRR 2005, Robotics, Sparse Extended Information, Sparse Information Matrix