@inproceedings{96eb8d8016624f4fa04ad2b212f6f6b8,
title = "Global localization of Monte Carlo localization based on multi-objective particle swarm optimization",
abstract = "Premature convergence often happens when a Monte Carlo localization (MCL) algorithm tries to localize a robot under highly symmetrical environments. In this paper, we propose a novel method of solving such problem for global localization by incorporating a multi-objective evolutionary approach to resample particles with two objectives, including particle weights and population distribution. By employing a multi-objective particle swarm optimization (MOPSO), our approach is capable of enhancing the exploration ability to improve population diversity while maintaining convergence quality to successfully localize the global optima. Simulation results have confirmed that localization performance using the proposed approach is significantly improved.",
keywords = "Global Localization, Monte Carlo localization, Multi-Objective Particle Swarm Optimization, Premature Convergence",
author = "Chien, {Chiang Heng} and Hsu, {Chen Chien} and Wang, {Wei Yen} and Kao, {Wen Chung} and Chien, {Chiang Ju}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 6th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2016 ; Conference date: 05-09-2016 Through 07-09-2016",
year = "2016",
month = oct,
day = "25",
doi = "10.1109/ICCE-Berlin.2016.7684728",
language = "English",
series = "IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin",
publisher = "IEEE Computer Society",
pages = "96--97",
editor = "Tom Wilson and Wolfgang Endemann and Cycon, {Hans L.} and Dietmar Hepper and Flores-Arias, {Jose Maria}",
booktitle = "2016 IEEE 6th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2016",
}