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: | https://fse.studenttheses.ub.rug.nl/id/eprint/9313 |
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