Automatic Update of Indoor Location Fingerprints with Pedestrian Dead Reckoning

Taniuchi, Daisuke; Maekawa, Taira

doi:10.1145/2667226

Cited by 22 publications

(5 citation statements)

References 32 publications

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“…Thus, although data for a specific area is collected and the database is already created, data collection should not be discontinued. Conversely, according to the related literature [52][53][54][55], data collection and database updating are frequently required to keep the pace with the frequent changes in indoor environments. Knowing that the equipped area lacks a database (i.e., requires initial generation) or that the database is already created (i.e., requires database updating) is beneficial for the data collection process for the following reason: if a specific area lacks localized signatures, the system collects crowdsourced data with a sampling rate higher than that used for updating to accelerate the initial generation and condense the collected signatures by each RP.…”

Section: -Initial Database Generation or Updatingmentioning

confidence: 99%

SUNS: A User-Friendly Scheme for Seamless and Ubiquitous Navigation Based on an Enhanced Indoor-Outdoor Environmental Awareness Approach

Mansour

Chen

2022

Remote Sensing

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Ubiquitous and seamless indoor-outdoor (I/O) localization is the primary objective for gaining more user satisfaction and sustaining the prosperity of the location-based services (LBS) market. Regular users, on the other hand, may be unaware of the impact of activating multiple localization sources on localization performance and energy consumption, or may lack experience deciding when to enable or disable localization sources in different environments. Consequently, an automatic handover mechanism that can handle these decisions on a user’s behalf can appreciably improve user satisfaction. This study introduces an enhanced I/O environmental awareness service that provides an automated handover mechanism for seamless navigation based on multi-sensory navigation integration schemes. Moreover, the proposed service utilizes low-power consumption sensor (LPCS) indicators to execute continuous detection tasks and invoke GNSS in confusion scenarios, and transition intervals to make the most firm decision on the credibility of the LPCS-triggered transition and compensate for indicator thresholds. In this manner, GNSS are used for short intervals that help reduce detection latency and power consumption. Consequently, the proposed service guarantees accurate and reliable I/O detection while preserving low power consumption. Leveraging the proposed service as an automated handover helped realize seamless indoor-outdoor localization with less switching latency, using an integrated solution based on extended Kalman filter. Furthermore, the proposed energy-efficient service was utilized to confine crowdsourced data collection to the required areas (indoors and semi-indoors) and prevent excess data collection outdoors, thereby reducing power drainage. Accordingly, the negative impact of data collection on the user’s device can be mitigated, participation can be encouraged, and crowdsourcing systems can be widely adopted.

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Section: -Initial Database Generation or Updatingmentioning

confidence: 99%

SUNS: A User-Friendly Scheme for Seamless and Ubiquitous Navigation Based on an Enhanced Indoor-Outdoor Environmental Awareness Approach

Mansour

Chen

2022

Remote Sensing

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“…Rai et al [36] also attempt to automatically construct radio maps with the pedestrian dead reckoning (PDR) technique. Similarly, Taniuchi et al [43] collect fingerprints with PDR techniques for automatic radio map update.…”

Section: Wi-fi Indoor Positioning With No/few Training Datamentioning

confidence: 99%

Predicting location semantics combining active and passive sensing with environment-independent classifier

Tachikawa

Maekawa

Matsushita

2016

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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This paper presents a method for estimating a user's indoor location without using training data collected by the user in his/her environment. Specifically, we attempt to predict the user's location semantics, i.e., location classes such as restroom and meeting room. While indoor location information can be used in many real-world services, e.g., context-aware systems, lifelogging, and monitoring the elderly, estimating the location information requires training data collected in an environment of interest. In this study, we combine passive sensing and active sound probing to capture and learn inherent sensor data features for each location class using labeled training data collected in other environments. In addition, this study modifies the random forest algorithm to effectively extract inherent sensor data features for each location class. Our evaluation showed that our method achieved about 85% accuracy without using training data collected in test environments.

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“…Traditional Wi-Fi fingerprinting typically involves a point-to-point collection method, which is laborconsuming and inefficient [11]. The crowdsourcing-based Wi-Fi fingerprinting collection approach is developed using public daily-life trajectories, and the accuracy of the generated navigation database is subject to the performance of raw collected trajectories [12], the number of reference points [13], and the bias of different devices [14].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization

Zhang

Chen

et al. 2023

Remote Sensing

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Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an intelligent 3D indoor localization framework that uses the integration of Wi-Fi, Bluetooth Low Energy (BLE), quick response (QR) code, and micro-electro-mechanical system sensors (the 3D-WBQM framework). An enhanced inertial odometry was developed for accurate pedestrian localization and trajectory optimization in indoor spaces containing magnetic interference and external acceleration, and Wi-Fi fine time Measurement stations, BLE nodes, and QR codes were applied for landmark detection to provide an absolute reference for trajectory optimization and crowdsourced navigation database construction. In addition, the robust unscented Kalman filter (RUKF) was applied as a generic integrated model to combine the estimated location results from inertial odometry, BLE, QR, Wi-Fi FTM, and the crowdsourced Wi-Fi fingerprinting for large-scale indoor positioning. The experimental results indicated that the proposed 3D-WBQM framework was verified to realize autonomous and accurate positioning performance in large-scale indoor areas using different location sources, and meter-level positioning accuracy can be acquired in Wi-Fi FTM supported areas.

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Automatic Update of Indoor Location Fingerprints with Pedestrian Dead Reckoning

Cited by 22 publications

References 32 publications

SUNS: A User-Friendly Scheme for Seamless and Ubiquitous Navigation Based on an Enhanced Indoor-Outdoor Environmental Awareness Approach

SUNS: A User-Friendly Scheme for Seamless and Ubiquitous Navigation Based on an Enhanced Indoor-Outdoor Environmental Awareness Approach

Predicting location semantics combining active and passive sensing with environment-independent classifier

Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization

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