A provably consistent method for imposing sparsity in feature-based SLAM information filters

Summary

Matthew Walter, Ryan Eustice and John Leonard, A provably consistent method for imposing sparsity in feature-based SLAM information filters. In Proceedings of the International Symposium on Robotics Research, pages 214-234, San Francisco, CA, USA, October 2005.

Abstract

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 posterior. In this manner, the Exactly Sparse Extended Information Filter (ESEIF) performs exact inference, employing a model which is conservative relative to the standard distribution. We demonstrate our algorithm both in simulation as well as on two nonlinear datasets, comparing it against the standard EKF as well as the Sparse Extended Information Filter (SEIF) by Thrun et al. The results convincingly show that our method yields conservative estimates for the robot pose and map which are nearly identical to those of the EKF in comparison to the SEIF formulation which results in overconfident error bounds.

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