Kuipers, J.T. (2010) Negative Landmark Information Influence in FastSLAM. Bachelor's Thesis, Artificial Intelligence.
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Abstract
The solution to Simultaneous Localisation And Mapping (SLAM) has been the Extended Kalman Filter (EKF) approach for a long time. A more recent and better performing solution based on a particle filter is called FastSLAM. There are several shortcomings to this solution to operate in the real world. The shortcoming we aim at is the assumption that all landmarks are certain. The possibility of wrong observations or moving landmarks are omitted. We will introduce two extentional methods in FastSLAM to take this landmark uncertainty in account. Both methods will clean up the map by deleting very uncertain landmarks. We will compare both methods with the original FastSLAM method, our results are promising but also show that a solution to real world SLAM needs more research.
| Item Type: | Thesis (Bachelor's Thesis) |
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| 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/9362 |
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