A sensor fusion approach for localization with cumulative error elimination

Feihu Zhang, Hauke Stahle, Guang Chen, Carsten Simon, Chao Chen, Christian Buckl, Alois Knoll

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

36 Scopus citations

Abstract

This paper describes a robust approach which improves the precision of vehicle localization in complex urban environments by fusing data from GPS, gyroscope and velocity sensors. In this method, we apply Kalman filter to estimate the position of the vehicle. Compared with other fusion based localization approaches, we process the data in a public coordinate system, called Earth Centred Earth Fixed (ECEF) coordinates and eliminate the cumulative error by its statistics characteristics. The contribution is that it not only provides a sensor fusion framework to estimate the position of the vehicle, but also gives a mathematical solution to eliminate the cumulative error stems from the relative pose measurements (provided by the gyroscope and velocity sensors). The experiments exhibit the reliability and the feasibility of our approach in large scale environment.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2012 - Conference Proceedings
Pages1-6
Number of pages6
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2012 - Hamburg, Germany
Duration: 13 Sep 201215 Sep 2012

Publication series

NameIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

Conference

Conference2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2012
Country/TerritoryGermany
CityHamburg
Period13/09/1215/09/12

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