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Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model
Joint Authors
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-14
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Jun& Takahashi, Yasutake. 2019. Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model. Journal of Sensors،Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1187464
Modern Language Association (MLA)
Wang, Jun& Takahashi, Yasutake. Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model. Journal of Sensors No. 2019 (2019), pp.1-19.
https://search.emarefa.net/detail/BIM-1187464
American Medical Association (AMA)
Wang, Jun& Takahashi, Yasutake. Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1187464
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187464