On the complexity and consistency of UKF-based SLAM

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

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

31 Scopus citations

Abstract

This paper addresses two key limitations of the unscented Kalman filter (UKF) when applied to the simultaneous localization and mapping (SLAM) problem: The cubic, in the number of states, computational complexity, and the inconsistency of the state estimates. In particular, we introduce a new sampling strategy that minimizes the linearization error and whose computational complexity is constant (i.e., independent of the size of the state vector). As a result, the overall computational complexity of UKF-based SLAM becomes of the same order as that of the extended Kalman filter (EKF) when applied to SLAM. Furthermore, we investigate the observability properties of the linear-regression-based model employed by the UKF, and propose a new algorithm, termed the Observability-Constrained (OC)-UKF, that improves the consistency of the state estimates. The superior performance of the OC-UKF compared to the standard UKF and its robustness to large linearization errors are validated by extensive simulations.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Robotics and Automation, ICRA '09
Pages4401-4408
Number of pages8
DOIs
StatePublished - Nov 2 2009
Event2009 IEEE International Conference on Robotics and Automation, ICRA '09 - Kobe, Japan
Duration: May 12 2009May 17 2009

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2009 IEEE International Conference on Robotics and Automation, ICRA '09
CountryJapan
CityKobe
Period5/12/095/17/09

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