Improving Covariance Matrix Diagonalization in SLAM of Mobile Robot
Diagonalization of covariance matrix through eigenvalue approach in extended Kalman Filter (EKF)-based simultaneous localization and mapping (SLAM) of mobile robot has been studied, as one of the possible approaches in reducing complexity hence computational cost of the system. However, the estimati...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/32672/1/Improving%20Covariance%20Matrix.pdf http://umpir.ump.edu.my/id/eprint/32672/ https://doi.org/10.1007/978-981-16-2406-3_73 |
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| Summary: | Diagonalization of covariance matrix through eigenvalue approach in extended Kalman Filter (EKF)-based simultaneous localization and mapping (SLAM) of mobile robot has been studied, as one of the possible approaches in reducing complexity hence computational cost of the system. However, the estimation is seemed to be too optimistic, and further investigation need to be conducted. In this paper, the effect on addition of Pseudo elements in the diagonalization process is investigated. It is evaluated at the updated state covariance matrix of EKF-based SLAM. It is found that the additional of pseudo components in diagonal matrix can improve the covariance matrix and lower the computational complexity. This finding has been proved through simulation. |
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