Abstract
An open problem in Simultaneous Localization and Map** (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.
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Walter, M., Eustice, R., Leonard, J. (2007). A Provably Consistent Method for Imposing Sparsity in Feature-Based SLAM Information Filters. In: Thrun, S., Brooks, R., Durrant-Whyte, H. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48113-3_20
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DOI: https://doi.org/10.1007/978-3-540-48113-3_20
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