Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model

• 資料種別: 文書・図像類
• タイトル: Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model
• 著者・編者: Jun, Wang; Yasutake, TAKAHASHI
• 著者標目: Jun, Wang; Yasutake, TAKAHASHI
• 出版事項: Hindawi
• 出版年月日等: 2019-10-14
• 出版年（W3CDTF）: 2019-10-14
• タイトル（掲載誌）: Journal of Sensors
• 巻号年月日等（掲載誌）: 2019
• 掲載巻: 2019
• ISSN（掲載誌）: ISSN : 1687-725X; ISSN : 1687-7268
• 本文の言語コード: eng
• 対象利用者: 一般
• 一般注記: 出版タイプ： AO; HF-band radio-frequency identification (RFID) is a robust identification system that is rarely influenced by objects in the robot activity area or by illumination conditions. An HF-band RFID system is capable of facilitating a reasonably accurate and robust self-localization of indoor mobile robots. An RFID-based self-localization system for an indoor mobile robot requires prior knowledge of the map which contains the ID information and positions of the RFID tags used in the environment. Generally, the map of RFID tags is manually built. To reduce labor costs, the simultaneous localization and mapping (SLAM) technique is designed to localize the mobile robot and build a map of the RFID tags simultaneously. In this study, multiple HF-band RFID readers are installed on the bottom of an omnidirectional mobile robot and RFID tags are spread on the floor. Because the tag detection process of the HF-band RFID system does not follow a standard Gaussian distribution, extended Kalman filter- (EKF-) based landmark updates are unsuitable. This paper proposes a novel SLAM method for the indoor mobile robot with a non-Gaussian detection model, by using the particle smoother for the landmark mapping and particle filter for the self-localization of the mobile robot. The proposed SLAM method is evaluated through experiments with the HF-band RFID system which has the non-Gaussian detection model. Furthermore, the proposed SLAM method is also evaluated by a range and bearing sensor which has the standard Gaussian detection model. In particular, the proposed method is compared against two other SLAM methods: FastSLAM and SLAM methods utilize particle filter for both the landmark updating and robot self-localization. The experimental results show the validity and superiority of the proposed SLAM method.
• 著作権情報: Copyright (C) 2019 Jun Wang and Yasutake Takahashi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
• 関連情報（DOI）: 10.1155/2019/3717298; https://doi.org/10.1155/2019/3717298
• 連携機関・データベース: 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• 提供元機関・データベース: 福井大学 : 福井大学学術機関リポジトリ