SURFとQ学習を用いた自律移動ロボットのためのランドマーク検出手法

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2017/09/01

タイトル(英語): The Landmark Detection Method using SURF and Q-Learning for an Autonomous Mobile Robot

著者名: 曽我　健太（関東学院大学 理工学部），原　翔悟（関東学院大学 理工学部），元木　誠（関東学院大学 理工学部），佐々木　清吾（防衛大学校　電気電子工学科）

著者名(英語): Kenta Soga (Department of Science and Engineering,Kanto Gakuin University), Syogo Hara (Department of Science and Engineering,Kanto Gakuin University), Makoto Motoki (Department of Science and Engineering,Kanto Gakuin University), Seigo Sasaki (Department of Electrical and Electronic Engineering, National Defense Academy of Japan)

キーワード: ランドマーク検出，SURF，Q学習，自律移動ロボット，AdaBoost　　Landmark Detection，SURF，Q-Learning，Autonomous Mobile Robot，AdaBoost

要約(英語): It is needs self-localization without GPS, when an autonomous mobile robot is doing in indoor. One of the landmark detection is used self-localization method. Conventional landmark detection methods were used template matching or color data. However, it is difficult to the landmark detection using conventional methods in real environment, because the landmark detection requires a lot of template. In addition, the landmark detection methods proposed using Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). Among them, SURF is strong to scale-change, rotation, and varying illumination. But, the detection methods using features cannot detect the landmark from many features similar to the landmark in real environment. Therefore, in this study, we build several landmark detectors using SURF and environment information is strong to scale-change, rotation, and varying illumination. In addition, we build a rule to select the best detector from these detectors by the reinforcement learning. We examine usefulness of the proposed method by comparing a landmark detection rate of the conventional method that is the AdaBoost detection using Haar-like features. The experiment result shows that the proposed method surpasses the landmark detection rate than the conventional method.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.137 No.9 （2017） 特集：知能メカトロニクス分野と連携する知覚情報技術

本誌掲載ページ: 1248-1257 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/137/9/137_1248/_article/-char/ja/