Continuously tracking of moving object by a combination of ultra-high frequency radio-frequency identification and laser range finder

Fu, Yulu; Liu, Ran; Zhang, Hua; Liang, Gaoli; Réhman, Shafiq Ur; Liu, Lixiang

doi:10.1177/1550147719860990

Cited by 8 publications

(3 citation statements)

References 41 publications

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“…The two most important parameters in DBSCAN algorithm are the radius

and number of laser spots

in each cluster. Our previously published paper [ 40 ] showed a comparison of the clustering performance under different

and

. The parameters of DBSCAN are not discussed in the article in order to save the space of the paper.…”

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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“…The two most important parameters in DBSCAN algorithm are the radius

and number of laser spots

in each cluster. Our previously published paper [ 40 ] showed a comparison of the clustering performance under different

and

. The parameters of DBSCAN are not discussed in the article in order to save the space of the paper.…”

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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“…Passive RFID sensors are less expensive than active sensors [13]. High frequency (HF) band RFID has been used to improvise precision [14]. Although RFID is useful in identifying objects, it cannot achieve each application's goal that requires human identification.…”

Section: Introductionmentioning

confidence: 99%

“…FIGURE14. Estimation of position.Two strings are generated from three points selected in the previous phase B1, B5, and B12.…”

mentioning

confidence: 99%

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Ali¹,

Liu²,

He³

et al. 2021

IEEE Access

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Object identification and localization in indoor and outdoor environments are paramount issues in object-human interaction. Recent advancements in the data fusion capabilities of multi-sensor systems have paved the way for research on emerging object identification and positioning techniques. This review describes techniques and methods used in positioning technologies. State-of-the-art localization technologies are classified into range-based, range-free and AI-based categories. An in-depth analysis of localization approaches based on laser range finder, radio-frequency identification, ultra-wideband, inertial measurement unit, etc., are presented by providing a detailed comparison based on range, accuracy, measurement method, advantages, disadvantages, and their applications. Furthermore, we investigate stateof-the-art multimodal data fusion techniques that utilize probabilistic methods for the precise estimation of object identification in motion and its localization.INDEX TERMS Indoor and outdoor positioning systems; identification and localization; unstructured environment; multi-sensor system; multi-object; positioning and localization techniques.

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Indoor and Outdoor Localization Methods for Advanced Navigation Systems

Kotamraju

Nadipalli

Kumer

et al. 2021

Proceedings of International Conference on Communication and Artificial Intelligence

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Continuously tracking of moving object by a combination of ultra-high frequency radio-frequency identification and laser range finder

Cited by 8 publications

References 41 publications

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

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

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Indoor and Outdoor Localization Methods for Advanced Navigation Systems

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