Improvement of Computational Efficiency of Unscented Particle Filter by Automatically Adjusting the Number of Particles

DOI

10.2991/jrnal.2016.3.2.14How to use a DOI?

Keywords

RoboCup, Self-localization, Unscented particle filter, Kalman filter, Particle

Abstract

In RoboCup Standard Platform League (SPL), the method using unscented particle filter (UPF) has been proposed for self-localization. The UPF resolves a problem of particle filter which cannot be sampled appropriately when the likelihood is too high or low. This filter can estimate accurate position when the more number of particles is. However, the more, the more computation time is needed. In the present paper, we propose an automatic adjustment method for the number of particles in UPF. The proposed method uses three kinds of feature values with respect to particles, i.e. centroid, standard deviation, and weight. Through computer simulations, we confirmed the improvement of computational efficiency of UPF?

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - JOUR
AU  - Kenta Hidaka
AU  - Takuo Suzuki
AU  - Kunikazu Kobayashi
PY  - 2016
DA  - 2016/09/01
TI  - Improvement of Computational Efficiency of Unscented Particle Filter by Automatically Adjusting the Number of Particles
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 132
EP  - 135
VL  - 3
IS  - 2
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2016.3.2.14
DO  - 10.2991/jrnal.2016.3.2.14
ID  - Hidaka2016
ER  -