Authors
Kenta Hidaka, Takuo Suzuki, Kunikazu Kobayashi
Corresponding Author
Kenta Hidaka
Available Online 1 September 2016.
DOI
https://doi.org/10.2991/jrnal.2016.3.2.14
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 ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).