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http://dx.doi.org/10.5391/JKIIS.2011.21.4.493

GraphSLAM Improved by Removing Measurement Outliers  

Kim, Ryun-Seok (연세대학교 전자전기공학과)
Choi, Hyuk-Doo (연세대학교 전자전기공학과)
Kim, Eun-Tai (연세대학교 전자전기공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.4, 2011 , pp. 493-498 More about this Journal
Abstract
This paper presents the GraphSLAM improved by selecting the measurement with respect to their likelihoods. GraphSLAM estimates the robot's path and map by utilizing the entire history of input data. However, GraphSLAM's performance suffers a lot from severely noisy measurements. In this paper, we present GraphSLAM improved by the selective measurement method. Thus the presented GraphSLAM provides higher performance compared with the standard GraphSLAM.
Keywords
GraphSLAM; selective measurement method; simultaneous localization and mapping(SLAM);
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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