MPiLoc: Self-Calibrating Multi-Floor Indoor Localization Exploiting Participatory Sensing

Luo, Chengwen; Hong, Hande; Chan, Mun Choon; Li, Jianqiang; Zhang, Xinglin; Ming, Zhong

doi:10.1109/tmc.2017.2698453

Cited by 53 publications

(19 citation statements)

References 33 publications

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“…Based on two fingerprint observations, we have no knowledge about how accurate is the relative orientation between two poses, we therefore set θ n,j m,i to zero and give a very large covariance value (i.e., 1000) to the edge, meaning that we are not able to infer the relative angle from two radio fingerprint observations. For users starting from arbitrary locations, we refer to [54] [55] [56] [57] to merge the paths based on the radio measurements and activity landmarks in our future work.…”

Section: F Merging Tracks At Different Timesmentioning

confidence: 99%

Collaborative SLAM Based on WiFi Fingerprint Similarity and Motion Information

Liu

Marakkalage

Padmal

et al. 2020

IEEE Internet Things J.

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Simultaneous localization and mapping (SLAM) has been extensively researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the widespread deployment of Wi-Fi wireless network. This paper presents a novel approach for collaborative simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system uses received signal strengths (RSS) from Wi-Fi access points (AP) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smart phone, without a prior knowledge about map or distribution of AP in the environment. We claim a loop closure based on the similarity of the two radio fingerprints. To further improve the performance, we incorporate the turning motion and assign a small uncertainty value to a loop closure if a matched turning is identified. The experiment was done in an area of 130 meters by 70 meters and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 meters with Tango-based PDR and 4.76 meters with a step counter-based PDR.

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Section: F Merging Tracks At Different Timesmentioning

confidence: 99%

Collaborative SLAM Based on WiFi Fingerprint Similarity and Motion Information

Liu

Marakkalage

Padmal

et al. 2020

IEEE Internet Things J.

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“…For the aforementioned techniques (RSSI and ToA), a Wireless Sensor Network (WSN) composed of a set of nodes deployed across the area under monitoring is utilized due to its low-consumption, self-organizing and easy deployment capabilities. Extensive research has been conducted in the literature to address the person tracking problem using RSSI [7], [14], [15], [19]. These proposals are mainly classified into deterministic or statistical methods.…”

Section: Introductionmentioning

confidence: 99%

SWiBluX: Multi-Sensor Deep Learning Fingerprint for Precise Real-Time Indoor Tracking

et al. 2019

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Indoor/outdoor localization topic has gained a significant research interest due to the wide range of potential applications. Commonly, the Fingerprinting methods for spatial characterization of the environments monitored are employed in deterministic/statistical estimation. However, there are Fingerprint parameters that are generally neglected and can seriously affect the performance yielding to low accurate location. Nowadays, machine and deep learning (DL) methods are employed in this topic due to its ability to approximate complex non-linear models being capable of mitigating the undesirable effects of wireless propagation. In this paper, a complete overview of most influential aspects in Fingerprinting and indoor tracking methods is presented. Furthermore, a novel multi-modal complete tracking system, called SWiBluX, based on statistic and DL techniques is presented. The system relies on relevant feature extraction from available data sources to estimate user's/target indoor position using a multi-phase statistical Fingerprint and DL disruptive approach. In addition, a Gaussian outlier filter is applied to the position estimation model output to further reduce the error in the estimation. The set of experiments performed shows that Fingerprint positioning accuracy estimation can be improved up to 45% resulting in a final estimation error that outperforms related literature.

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“…Indoor localization has always been an urgently needed service in our society, which can be used for indoor navigation, daily activities tracking and many other amazing applications [1], [2]. Compared with outdoor localization, indoor localization, without GPS signal, faces many challenges.…”

Section: Introductionmentioning

confidence: 99%

Online Learning-Based WIFI Radio Map Updating Considering High-Dynamic Environmental Factors

Niu¹,

Zhang

Wang

et al. 2019

IEEE Access

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Indoor localization has been recognized as a promising research around the world, and fingerprint-based localization method which leverages WIFI Received Signal Strength (RSS) has been extensively studied since widespread deployment of Access Points (APs) makes WIFI signals omnipresent and easily be obtained. A primary weakness of WIFI-based fingerprinting localization approach lies in its vulnerability under environmental changes and alteration of AP deployment. Despite some studies focus on dealing with effects of AP alterations and low-dynamic environmental factors, such as humidity, temperature, etc., influences of high-dynamic factors, such as changes of crowds' density and position, on WIFI radio map have not been sufficiently studied. In this work, we propose OWUH, an Online Learning-based WIFI Radio Map Updating service considering influences of high-dynamic factors. OWUH utilizes sensors in smart phones as the source of RSS datasets, and it combines historical and newly collected RSS data and purposeful probe data as dataset to incrementally update radio map, which means, compared with traditional methods, the OWUH approach requires a smaller number of RSS data for frequent updating of radio map. Moreover, in order to further enlarge our dataset, we take static data and low-dynamic data into account. An improved online learning method is proposed to recognize periodic pattern and update current radio map. Extensive experiments with 15 volunteers across 10 days indicate that OWUH effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with mean errors of less than 5dB, outperforming existing approaches. INDEX TERMS Indoor localization, WIFI radio map, crowdsourcing, online learning, high-dynamic.

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MPiLoc: Self-Calibrating Multi-Floor Indoor Localization Exploiting Participatory Sensing

Cited by 53 publications

References 33 publications

Collaborative SLAM Based on WiFi Fingerprint Similarity and Motion Information

Collaborative SLAM Based on WiFi Fingerprint Similarity and Motion Information

SWiBluX: Multi-Sensor Deep Learning Fingerprint for Precise Real-Time Indoor Tracking

Online Learning-Based WIFI Radio Map Updating Considering High-Dynamic Environmental Factors

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