An Approach to Reduce Computational Cost for Localization Problem
One of the biggest factors that contribute to the computational cost of extended Kalman filter-based SLAM is the covariance update. This is due to the multiplications of the covariance matrix with other parameters and the increment of its dimension, which is twice the number of landmarks. Therefore...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/9786/1/An%20Approach%20to%20Reduce%20Computational%20Cost%20for%20Localization%20Problem.pdf http://umpir.ump.edu.my/id/eprint/9786/7/An%20Approach%20to%20Reduce%20Computational%20Cost%20for%20Localization%20Problem%20-%20Abstract.pdf http://umpir.ump.edu.my/id/eprint/9786/ |
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| Summary: | One of the biggest factors that contribute to the computational cost of extended Kalman filter-based SLAM is the covariance update. This is due to the multiplications of the covariance matrix with other parameters and the increment of its dimension, which is twice the number of landmarks. Therefore a study is conducted to find a possible technique to decrease the computational complexity of the covariance matrix without minimizing the accuracy of the state estimation. This paper presents a preliminary study on the matrixdiagonalization technique, which is applied to the covariance matrix in EKF-based SLAM to simplify the multiplication process. The behaviors of estimation and covariance are observed based on three case studies. |
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