A comparison of EKF, UKF, FastSLAM2.0, and UKF-based FastSLAM algorithms

Zeyneb Kurt-Yavuz*, Sirma Yavuz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

51 Citations (Scopus)

Abstract

This study aims to contribute a comparison of various simultaneous localization and mapping (SLAM) algorithms that have been proposed in literature. The performance of Extended Kalman Filter (EKF) SLAM, Unscented Kalman Filter (UKF) SLAM, EKF-based FastSLAM version 2.0, and UKF-based FastSLAM (uFastSLAM) algorithms are compared in terms of accuracy of state estimations for localization of a robot and mapping of its environment. The algorithms were run using the same type of robot on Player/Stage environment. The results show that the UKF-based FastSLAM has the best performance in terms of accuracy of localization and mapping. Unlike most of the previous applications of FastSLAM in literature, no waypoints are used in this study.

Original languageEnglish
Title of host publicationINES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings
Pages37-43
Number of pages7
DOIs
Publication statusPublished - 1 Oct 2012
Externally publishedYes
EventIEEE 16th International Conference on Intelligent Engineering Systems, INES 2012 - Lisbon, Portugal
Duration: 13 Jun 201215 Jun 2012

Publication series

NameINES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings

Conference

ConferenceIEEE 16th International Conference on Intelligent Engineering Systems, INES 2012
CountryPortugal
CityLisbon
Period13/06/1215/06/12

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