Efficient self-adjusting, similarity-based location fingerprinting with passive UHF RFID

Vorst, Philipp; Koch, Artur; Zell, Andreas

doi:10.1109/rfid-ta.2011.6068632

Cited by 20 publications

(6 citation statements)

References 27 publications

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“…These approaches estimate the position of a mobile agent from a set of precollected observations. They showed a slight improvement to model-based approaches [9]. However, the concern of this paper is mapping of RFID tags, which requires an explicit sensor model and it is almost impossible to take all environmental factors into account.…”

Section: Mapping Rfid Tagsmentioning

confidence: 99%

“…Furthermore, location-specific factors, such as the materials the tags are attached to, obstacles in the environment, orientation between tag and antenna or other radio devices in the environment, have high influence on the behavior of the RFID reader. Thus, many authors applied location-fingerprinting techniques to improve the localization accuracy of mobile agents [9], [10]. These approaches estimate the position of a mobile agent from a set of precollected observations.…”

Section: Mapping Rfid Tagsmentioning

confidence: 99%

See 1 more Smart Citation

Mapping UHF RFID tags with a mobile robot using a 3D sensor model

Liu

Koch

Zell

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Recently, researchers showed growing interest in utilizing UHF Radio-Frequency Identification (RFID) technology for localizing tagged items with mobile robots in industrial scenarios. In this paper we present a novel three-dimensional (3D) probability sensor model of RFID antennas in the context of mapping passive RFID tags with mobile robots. The proposed 3D sensor model characterizes both detection rates and received signal strength (RSS). Compared to 2D-sensor model based approaches, the 3D model gains a higher mapping accuracy for 2D position estimation. Specially, with this sensor model, we are able to localize the tags in 3D by integrating the measurements from a pair of RFID antennas mounted at different heights of the robot. Furthermore, by integrating negative information (i.e., non-detections), the 3D mapping accuracy can be improved. Additionally, we utilize KLD-sampling to reduce the number of particles for our specific application, so that our algorithm can be performed online. Indoor experiments with a Scitos G5 robot demonstrate the effectiveness of our approach. We also provide the datasets of this work for download.

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Section: Mapping Rfid Tagsmentioning

confidence: 99%

Section: Mapping Rfid Tagsmentioning

confidence: 99%

Mapping UHF RFID tags with a mobile robot using a 3D sensor model

Liu

Koch

Zell

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…In the first case, we test the filters by the TWOR circular trajectory in an indoor space with a constant motor displacement and measurement noise. In the second case, all filters are tested by experimental data available from [30].…”

Section: Self-localization In Rfid-tag Environmentsmentioning

confidence: 99%

TWOR: Improving Modeling and Self-Localization in RFID-Tag Networks Under Colored Noise

et al. 2023

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This paper discusses the three-wheeled omnidirectional robot (TWOR) self-localization in radio frequency identification (RFID) tag environments. The nonlinear TWOR model is significantly improved by using geometric interpretation and incremental time representation in discrete time. The TWOR position and heading are self-estimated using distance measurements to RFID tags and a digital gyroscope in the presence of typical colored measurement noise (CMN). The extended unbiased finite impulse response (EFIR) is developed along with the extended Kalman filter (EKF) and their versions, cEKF and cEFIR, modified for Gauss-Markov CMN. A particle filter is used as a benchmark. It is shown that the cEFIR filter is more robust than the cEKF and almost as robust as the particle filter, although the latter is less accurate in real time.INDEX TERMS TWOR modeling, self-localization, colored noise, extended unbiased FIR filter, extended Kalman filter.

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“…In this paper, it is necessary to find a reliable similarity measurement function to match the speed estimated by RFID due to the large number of clusters obtained by laser sensors. Vorst et al [ 34 , 35 ] compared different similarity algorithms in order to achieve self-localization with passive RFID fingerprints. They showed that the Bray–Curtis (BC) measure gives the best performance among all of the evaluated methods.…”

Section: Indoor Multiple Dynamic Objects Identification and Localimentioning

confidence: 99%

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID

Liu

Wang

et al. 2020

Sensors

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In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object’s radial velocities estimated by the RFID phase. The method also uses a fixed time series as “sliding time window” to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.

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Efficient self-adjusting, similarity-based location fingerprinting with passive UHF RFID

Cited by 20 publications

References 27 publications

Mapping UHF RFID tags with a mobile robot using a 3D sensor model

Mapping UHF RFID tags with a mobile robot using a 3D sensor model

TWOR: Improving Modeling and Self-Localization in RFID-Tag Networks Under Colored Noise

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID

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