Issue |
MATEC Web Conf.
Volume 160, 2018
International Conference on Electrical Engineering, Control and Robotics (EECR 2018)
|
|
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Article Number | 06005 | |
Number of page(s) | 6 | |
Section | Intelligent Robot Design and Control | |
DOI | https://doi.org/10.1051/matecconf/201816006005 | |
Published online | 09 April 2018 |
The Square Root UKF-SLAM Algorithm Based on the Smallest Proportion of Skewness in Single Line Sampling
1
Key Lab of Electric Drive and Control of Anhui Province, Anhui Polytechnic University, Wuhu 241000, China
2
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230027, China
In view of the distortion in the filter gain matrix calculation as well as the high computational complexity and the nonlocal effect of symmetric sampling that exists in the UKF-SLAM algorithm, the square root UKF-SLAM algorithm based on the smallest proportion of skewness in single line sampling was proposed. According to the mended algorithm, the square root of covariance matrix is brought into iteration operation instead of covariance matrix, moreover, the smallest proportion of skewness in single line sampling is utilized for the optimization of sampling strategy. The results of simulation show that the algorithm can effectively improve the position accuracy in robot as well as the estimation accuracy of feature map. Furthermore, the computational complexity is reduced and the algorithm stability is improved.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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