Strong Tracking Filter Simultaneous Localization and Mapping algorithm

Huiping Li, Demin Xu, Yao Yao, Fubin Zhang

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

1 Scopus citations

Abstract

Simultaneous Localization and Mapping (SLAM) is a central and complex problem in robot research community. In SLAM, Extended Kalman Filter (EKF) implementation is widely used to localize the robot and build the environment map incrementally. In this paper, we propose a Strong Tracking Filter (STF) SLAM algorithm. This algorithm applies STF to deal with the non-linear estimated problem in SLAM instead of EKF. It can make the performance of the nonlinear filter approximate to that of optimal linear Kalman Filter (KF), so it can construct highly accurate maps and locate the robot more accurately than EKF SLAM. Simulation experiments illustrate the superior performance of our approach compared to EKF SLAM algorithm.

Original languageEnglish
Title of host publicationProceedings - International Conference on Computer Science and Software Engineering, CSSE 2008
Pages1085-1088
Number of pages4
DOIs
StatePublished - 2008
EventInternational Conference on Computer Science and Software Engineering, CSSE 2008 - Wuhan, Hubei, China
Duration: 12 Dec 200814 Dec 2008

Publication series

NameProceedings - International Conference on Computer Science and Software Engineering, CSSE 2008
Volume1

Conference

ConferenceInternational Conference on Computer Science and Software Engineering, CSSE 2008
Country/TerritoryChina
CityWuhan, Hubei
Period12/12/0814/12/08

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