2A1-M03 識別訓練法によるDDFを用いた自己位置推定における最適パラメータ学習(移動ロボットの自己位置推定と地図構築)

概要

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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.
一般社団法人日本機械学会の論文
2011-05-26