Kuroda Yoji    Department   Undergraduate School  , School of Science and Technology    Position   Professor
Language	English
Publication Date	2010/12
Type	International Conference
Peer Review	Peer reviewed
Title	Discriminative Parameter Determination of Divided Difference Filter for Mobile Robot Localization
Contribution Type	Co-authored (other than first author)
Journal	Proc. of the 2010 IEEE Int'l Conf. on Robotics and Biomimetics (ROBIO2010)
Journal Type	Another Country
Publisher	IEEE
Volume, Issue, Page	pp.967-972
Author and coauthor	Yuto Fujii, Atsushi Sakai and Yoji Kuroda
Details	In this paper, we propose a learning method to solve the parameter determination problem of divided difference filter (DDF) for accurate localization. DDF can achieve comparatively accurate localization than other Kalman filter algorithms in poor GPS area. However, parameter determining process of DDF requires significant time and engineering cost. Furthermore, it is difficult to obtain optimal parameters for accurate localization by hand-tuning. DDF has three parameters which should be determined: covariance matrices of input and measurement noise and a Hyper-parameter. Our technique uses a discriminative learning method to determine these parameters. The proposal method absolves developers from the cumbersome process of parameter setting. This paper describes the efficiency of our technique through simulations and an experiment.
URL for researchmap	http://ieeexplore.ieee.org/document/5723457/