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The effect of using negative information on the accuracy of FastSLAM

Doornbos, M. (2010) The effect of using negative information on the accuracy of FastSLAM. Bachelor's Thesis, Artificial Intelligence.

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Abstract

For years, the standard algorithm for Simultaneous Localization And Mapping (SLAM) has been the Extended Kalman Filter (EKF). A more recent and more accurate solution is the FastSLAM-algorithm, which uses a Rao-Blackwellized particle filter. However, unreliable landmarks still pose a major problem to this algorithm. Two approaches to deal with these landmarks have been tested and compared to the performance of the original FastSLAM-algorithm. The results are promising, but not entirely applicable to real-world situations, for which further research will be needed.

Item Type: Thesis (Bachelor's Thesis)
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 15 Feb 2018 07:44
Last Modified: 15 Feb 2018 07:44
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/9313

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