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A Novel Hybrid Unscented Particle Filter based on Firefly Algorithm for Tightly-Coupled Stereo Visual-Inertial Vehicle Positioning

Published online by Cambridge University Press:  11 November 2019

Xiuyuan Li*
Affiliation:
(School of Instrument and Electronics, North University of China, Taiyuan, China)
Wenxue Gao
Affiliation:
(School of Instrument and Electronics, North University of China, Taiyuan, China)
Jiashu Zhang
Affiliation:
(School of Information and Communication Engineering, North University of China, Taiyuan, China)

Abstract

This paper presents a hybrid unscented particle filter (UPF) based on the firefly algorithm for tightly-coupled stereo visual-inertial vehicle positioning systems (VIVPS). Compared with standard UPF, this novel approach can achieve similar estimation accuracy with much less computational complexity. To reduce the computational complexity, the time updating of the hybrid unscented Kalman filter is conducted via the formula of standard linear Kalman filter on the basis of the constructed linear/nonlinear mixed filter model. The particle updating of the particle filter is optimised by modified firefly algorithm to reduce the number of particles needed by means of moving particles towards high likelihood regions via the attraction and movement of fireflies, leading to a significant reduction of computational complexity. Experimental results show the average execution time of the proposed approach is 23·8% that of the standard UPF with similar accuracy, indicating the designed method for tightly-coupled stereo VIVPS can better satisfy the real-time requirement of the system.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2019

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