Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
Abstract
The simultaneous localization and mapping (SLAM) problem is a fundamental requirement for autonomous robots that moves in unknown environment. The UFastSLAM is effective way for this purpose. This method improves the FastSLAM algorithm using unscented transform. However, the resampling process and unknown statistical information process and measurement noise lead to inconsistency. To improve UFastSLAM in terms of accuracy and consistency, in this article, the improved UFastSLAM using particle swarm optimization (PSO) and adaptive neuro-fuzzy inference system (ANFIS) is proposed. In this method, ANFIS estimates adaptively statistical characteristics of the noises and supervises the consistency. While PSO is used to modify samples. Especially when the statistical characteristics of the noises are unknown, the performance of other algorithms decreases while proposed method has high accuracy. In addition, compared to other methods, the proposed method less dependent on the number of particles and thus it provides greater accuracy with less computational cost.
Havangi, R. (2018). Improving of Simultaneous Localization and Mapping using Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 48(1), 431-442.
MLA
R. Havangi. "Improving of Simultaneous Localization and Mapping using Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System". TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 48, 1, 2018, 431-442.
HARVARD
Havangi, R. (2018). 'Improving of Simultaneous Localization and Mapping using Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System', TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 48(1), pp. 431-442.
VANCOUVER
Havangi, R. Improving of Simultaneous Localization and Mapping using Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 2018; 48(1): 431-442.
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