Efficient robot localization and SLAM algorithms using Opposition based High Dimensional optimization Algorithm
Particle filter (PF) is introduced to tackle the limitations of the Kalman filter which adopts Gaussian in the state and noise of the system. PFs have the problem of sample impoverishment and one approach to solve this problem is to optimize the proposal distribution shown by particles. This paper i...
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| Veröffentlicht in: | Engineering applications of artificial intelligence Jg. 104; S. 104308 |
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| Sprache: | Englisch |
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01.09.2021
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| Abstract | Particle filter (PF) is introduced to tackle the limitations of the Kalman filter which adopts Gaussian in the state and noise of the system. PFs have the problem of sample impoverishment and one approach to solve this problem is to optimize the proposal distribution shown by particles. This paper introduces a novel evolutionary PF based on Opposition based High Dimensional optimization Algorithm (OHDA) to reposition the particles of PF in high probable regions for estimation. OHDA will preserve the diversity of particles while emphasizing the more informative ones by information sharing and angular movement operators. Opposite particles are introduced in this paper to speed up the convergence of PF. Virtual forward movement by angular movement of OHDA is employed to better guide the search process. The optimized PF can improve the performance of the estimation algorithms in problems such as localization and SLAM. In robot localization problem, particles show the location of the robot in a known environment. For SLAM (Simultaneous Localization And Mapping), particles contain the location of the robot as well as estimated map of the environment. The application of the resulting evolutionary particle filter is tested in both localization and SLAM. Comparing the results of the proposed evolutionary particle filter with other algorithms confirms the efficiency of applying OHDA to PF in terms of improving estimation accuracy in the well-known Victoria park dataset and some other generated test environments. Comparing optimization algorithms on FASTSLAM and UFASTSLAM are PSO, FA, MVO, and MGWO. |
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| AbstractList | Particle filter (PF) is introduced to tackle the limitations of the Kalman filter which adopts Gaussian in the state and noise of the system. PFs have the problem of sample impoverishment and one approach to solve this problem is to optimize the proposal distribution shown by particles. This paper introduces a novel evolutionary PF based on Opposition based High Dimensional optimization Algorithm (OHDA) to reposition the particles of PF in high probable regions for estimation. OHDA will preserve the diversity of particles while emphasizing the more informative ones by information sharing and angular movement operators. Opposite particles are introduced in this paper to speed up the convergence of PF. Virtual forward movement by angular movement of OHDA is employed to better guide the search process. The optimized PF can improve the performance of the estimation algorithms in problems such as localization and SLAM. In robot localization problem, particles show the location of the robot in a known environment. For SLAM (Simultaneous Localization And Mapping), particles contain the location of the robot as well as estimated map of the environment. The application of the resulting evolutionary particle filter is tested in both localization and SLAM. Comparing the results of the proposed evolutionary particle filter with other algorithms confirms the efficiency of applying OHDA to PF in terms of improving estimation accuracy in the well-known Victoria park dataset and some other generated test environments. Comparing optimization algorithms on FASTSLAM and UFASTSLAM are PSO, FA, MVO, and MGWO. |
| ArticleNumber | 104308 |
| Author | GhaemiDizaji, Manizheh Dadkhah, Chitra Leung, Henry |
| Author_xml | – sequence: 1 givenname: Manizheh surname: GhaemiDizaji fullname: GhaemiDizaji, Manizheh email: m_ghaemi@email.kntu.ac.ir organization: Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran – sequence: 2 givenname: Chitra orcidid: 0000-0002-9836-9388 surname: Dadkhah fullname: Dadkhah, Chitra email: dadkhah@kntu.ac.ir organization: Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran – sequence: 3 givenname: Henry surname: Leung fullname: Leung, Henry email: leungh@ucalgary.ca organization: Department of Electrical and Computer Engineering, University of Calgary, Alberta, Canada |
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| Cites_doi | 10.1109/TEVC.2008.2011729 10.1016/j.ijleo.2019.06.013 10.1504/IJBIC.2010.032124 10.1109/TRO.2008.924946 10.1016/j.advwatres.2017.11.011 10.1016/j.dsp.2018.06.015 10.1109/MRA.2006.1638022 10.1016/j.asoc.2020.106185 10.1016/j.ijleo.2015.05.028 10.1007/s11633-016-1050-y 10.1007/s10846-013-0009-2 10.1049/iet-cvi.2016.0201 10.1007/s00521-015-1870-7 10.1177/1729881419839575 10.1109/TIE.2018.2854557 10.1109/ACCESS.2019.2934995 10.1016/j.ins.2005.02.003 10.1016/j.dt.2019.10.004 10.1016/j.ijleo.2017.11.155 10.1016/j.eswa.2019.06.006 10.1016/j.asoc.2019.04.014 10.1016/j.eswa.2014.03.012 10.1109/TIE.2016.2522382 10.1109/TMECH.2014.2311416 10.1016/j.robot.2006.02.009 |
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| Keywords | FASTSLAM Particle Filter Unscented FASTSLAM Opposition based High Dimensional Optimization Localization |
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| SubjectTerms | FASTSLAM Localization Opposition based High Dimensional Optimization Particle Filter Unscented FASTSLAM |
| Title | Efficient robot localization and SLAM algorithms using Opposition based High Dimensional optimization Algorithm |
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