Pedestrian dead reckoning with novel heading estimation under magnetic interference and multiple smartphone postures

Li, Wei; Chen, Ruizhi; Yu, Yue; Wu, Yuan; Zhou, Haitao

doi:10.1016/j.measurement.2021.109610

Cited by 26 publications

(10 citation statements)

References 34 publications

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“…It relies on the inertial sensors extensively existing in mobile devices, e.g., smartphones, to acquire information about the user’s movements, which are then combined with the user’s previous location to estimate the present position and further achieve complete trajectory. The equation utilized for location estimation is as follows:

where

is the pedestrian position at time

is the step length, and

details the heading direction of the pedestrian [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones

Gao

Konomi

2022

Sensors

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Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.

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where

is the pedestrian position at time

is the step length, and

details the heading direction of the pedestrian [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones

Gao

Konomi

2022

Sensors

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“…The dead-reckoning (DR) technique is to estimate the present location of a movable item using a previously calculated location, and then integrating estimated of speed, heading direction, and course over time taken is known as Dead Reckoning (DR) [18].…”

Section: E Dead Reckoning Techniquementioning

confidence: 99%

“…The employed localization technique is dead reckoning, which is referenced to as PDR in the case of pedestrian navigation. Sensor noises and interference problems (particularly indoors) influence accelerometer and magnetometer data, while gyroscope readings have a large drift over just a few seconds to determine the heading [18], [132]. Another typical filtering method for reducing sensor drift rates and estimating current users' position is recommended to be employed in which includes a Kalman or particle filters [133], [134].…”

Section: Inertial Sensorsmentioning

confidence: 99%

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

Asaad¹,

Maghdid²

2021

Preprint

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<p>The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since, most of the people daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT enabled technologies in health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated.</p>

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“…PDR can be completed only by using low-cost sensors of mobile phones. It does not need the assistance of external satellite signals or location base stations [8,9]. However, due to the false data collected by low-cost smartphone sensors and the disadvantage that the accuracy of PDR decreases over time, single PDR algorithm is difficult to meet the needs of long-term localization and navigation [10].…”

Section: Introductionmentioning

confidence: 99%

An Outdoor Pedestrian Localization Scheme Fusing PDR and VPR

Liu

Gong

Wang

et al. 2022

Wireless Communications and Mobile Computing

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With the popularity of phones, the demand for pedestrian location-based services has increased significantly. PDR (pedestrian dead reckoning algorithm) can provide pedestrians with real-time and continuous localization services in the outdoor environment. However, due to the accuracy of the built-in inertial sensor of the mobile phone collecting data, there is heading drift in PDR resulting in localization error. The localization method based on VPR (visual place recognition) can provide the accurate position of a single point. The VPR based on the lightweight model only generates a low amount of calculation. We propose an outdoor localization scheme fusing PDR and VPR to provide continuous localization service for outdoor pedestrian localization. The experiment results show that compared with the PDR algorithm, the ARMSE of the fusion localization scheme is reduced by 40% to 60%, and the final start-to-end error is reduced by 31.9 m. The fusion scheme can provide an accurate and continuous localization service.

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Pedestrian dead reckoning with novel heading estimation under magnetic interference and multiple smartphone postures

Cited by 26 publications

References 34 publications

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

An Outdoor Pedestrian Localization Scheme Fusing PDR and VPR

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