SLAM Method Based on Independent Particle Filters for Landmark Mapping and Localization for Mobile Robot Based on HF-band RFID System

Wang, Jun; Takahashi, Yasutake

doi:10.1007/s10846-017-0701-8

Cited by 11 publications

(18 citation statements)

References 18 publications

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“…A combination of SLAM method proposed in [33] with the RSS fingerprints method is considered necessary to complete this work. The use of the SLAM method is expected to solve the installation error and the problem presented by changes in the height of the ceiling.…”

Section: Discussionmentioning

confidence: 99%

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Indoor mobile robot self-localization based on a low-cost light system with a novel emitter arrangement

Wang

Takahashi

2018

Robomech J

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This paper proposes a low-cost infrared (IR) LED system with a novel arrangement that enables the self-localization of an indoor mobile robot. The proposed system uses only low-cost IR LED emitters installed on the ceiling of a building and low-cost IR receivers, each equipped with a photodiode, located on top of the mobile robot for detection. The IR LEDs, which are driven by an on-off keying modulator with various frequencies, are used as active landmarks. The mobile robot localizes itself based on the IR LED signals it receives. Although it would be desirable to assign a unique ID to each emitter, this would be difficult to realize because of the limited number of available frequencies and the production cost resulting from the use of independent microcontrollers. Therefore, we designed a novel landmark configuration consisting of an IR LED array with unique ID-encoding capabilities based on a combination of different frequencies and the repeated use of each ID encoding LED to address the problem of limited frequencies. The novel landmark arrangement reduces the costs associated with production, ID encoding, and computation. A Monte-Carlo localization method with a belief function is utilized to estimate the position and orientation of the mobile robot based on the decoded ID information and received signal strength. The proposed system was tested in a real environment and the experimental results confirmed the validity and accuracy of the system and method.

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Section: Discussionmentioning

confidence: 99%

“…An increase in the number of particles enables the accuracy of the estimation to be improved, whereupon the computational cost also increases [33]. An alternative approach to improve the accuracy would be to use additional emitters to shorten the interval between two adjacent emitters.…”

Section: Computational Cost Of the Proposed Systemmentioning

confidence: 99%

Indoor mobile robot self-localization based on a low-cost light system with a novel emitter arrangement

Wang

Takahashi

2018

Robomech J

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“…Wang and Takahashi [16] used independent particle filters [19] to achieve both landmark mapping and self-localization for a mobile robot utilizing the HF-band RFID system. To make the article concise, we refer to this SLAM method with the particle filter-based landmark updating as P-SLAM in the following description.…”

Section: Introductionmentioning

confidence: 99%

“…To make the article concise, we refer to this SLAM method with the particle filter-based landmark updating as P-SLAM in the following description. The differences between the P-SLAM and FastSLAM with extended Kalman filter (EKF) based landmark updating were also clarified in [16]. In addition, the theoretical and practical superiority of P-SLAM was demonstrated by comparing the method with FastSLAM, using the RFID detection model.…”

Section: Introductionmentioning

confidence: 99%

“…By adding a Gaussian random value during the motion model updating step for each robot particle at each loop of the SLAM procedure in [16], the degeneration of the particle filter for estimating robot self-localization can be prevented. However, the particles for estimating a stationary landmark are always updated within the states initialized at the first detection of this landmark by using the particle filter in [16]. The tag particles easily converge to a small number of particles at the resampling step and generate the degeneracy problem when using an RFID reader with a large detection range.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Takahashi

2019

Journal of Sensors

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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.

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Simultaneous Localization and Mapping Using the Phase of Passive UHF-RFID Signals

Martinelli

2018

J Intell Robot Syst

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SLAM Method Based on Independent Particle Filters for Landmark Mapping and Localization for Mobile Robot Based on HF-band RFID System

Cited by 11 publications

References 18 publications

Indoor mobile robot self-localization based on a low-cost light system with a novel emitter arrangement

Indoor mobile robot self-localization based on a low-cost light system with a novel emitter arrangement

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

Simultaneous Localization and Mapping Using the Phase of Passive UHF-RFID Signals

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