GraphSLAM Improved by Removing Measurement Outliers

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.

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