An observability-constrained sliding window filter for SLAM

Guoquan P. Huang, Anastasios I. Mourikis, Stergios Roumeliotis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

35 Scopus citations

Abstract

A sliding window filter (SWF) is an appealing smoothing algorithm for nonlinear estimation problems such as simultaneous localization and mapping (SLAM), since it is resource-adaptive by controlling the size of the sliding window, and can better address the nonlinearity of the problem by relinearizing available measurements. However, due to the marginalization employed to discard old states from the sliding window, the standard SWF has different parameter observability properties from the optimal batch maximum-a-posterior (MAP) estimator. Specifically, the nullspace of the Fisher information matrix (or Hessian) has lower dimension than that of the batch MAP estimator. This implies that the standard SWF acquires spurious information, which can lead to inconsistency. To address this problem, we propose an observability-constrained (OC)-SWF where the linearization points are selected so as to ensure the correct dimension of the nullspace of the Hessian, as well as minimize the linearization errors. We present both Monte Carlo simulations and real-world experimental results which show that the OC-SWF's performance is superior to the standard SWF, in terms of both accuracy and consistency.

Original languageEnglish (US)
Title of host publicationIROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Subtitle of host publicationCelebrating 50 Years of Robotics
Pages65-72
Number of pages8
DOIs
StatePublished - Dec 29 2011
Event2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11 - San Francisco, CA, United States
Duration: Sep 25 2011Sep 30 2011

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

Other2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11
CountryUnited States
CitySan Francisco, CA
Period9/25/119/30/11

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