Improving Pedestrian Dead Reckoning Using Likely Paths and Backtracking for Mobile Devices

Holzke, Fabian; Wolff, Johann-Peter; Haubelt, Christian

doi:10.1109/percomw.2019.8730734

Cited by 3 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indoor tracking accuracy is still a major issue in applications [18,28,63,73,103,105,108,125,135] due to issues in capturing signals [29]. As a result of information fusion, dependable and exact indoor localization services can be provided [10] to achieve good position estimation accuracy [27].…”

Section: A Environmentmentioning

confidence: 99%

“…Other studies have recommended integrating other indoor localization technologies, such as Wi-Fi, RFID, and Bluetooth, to improve localization accuracy [101]. Additional localization using coarse-grained Wi-Fi fingerprints could help score and validate alternative paths [125]. The threshold-based activity recognition cannot distinguish between different types of elevation, such as elevators, escalators, and stairs, especially in buildings where elevators pass across multiple floors [67].…”

Section: Recommendations Related To Accuracymentioning

confidence: 99%

“…Others proposed integrating other indoor localization technologies, such as Wi-Fi, RFID, and Bluetooth, to improve localization accuracy [101]. Additional localization using coarse-grained Wi-Fi fingerprints could help score and validate alternative paths [125].…”

Section: Recommendations Related To Technical Aspectsmentioning

confidence: 99%

“…Further studies are needed to evaluate the mechanism for improving recognition accuracy, the relationship between anchor node position and system performance, and the effects of step length [64]. The introduction of step resizing and trajectory shape matching to known pathways is imminent as well [125]. More studies are sought to look into the transition step when walking, especially in a situation where Wi-Fi gives bad readings because it affects the estimation results [67].…”

Section: Recommendations Related To Step Detectionmentioning

confidence: 99%

“…Time complexity is critical when assessing the performance efficiency of a system. The optimal classifier achieves the least time complexity while maintaining the highest accuracy [125]. 7.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

et al. 2023

View full text Add to dashboard Cite

These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps.

show abstract

Section: A Environmentmentioning

confidence: 99%

Section: Recommendations Related To Accuracymentioning

confidence: 99%

Section: Recommendations Related To Technical Aspectsmentioning

confidence: 99%

Section: Recommendations Related To Step Detectionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

et al. 2023

View full text Add to dashboard Cite

show abstract

Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM

Wang

Luo

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Accurate heading estimation is the foundation of numerous applications, including augmented reality, pedestrian dead reckoning, and human-computer interactions. While magnetometer is a key source of heading information, the poor accuracy of consumer-grade hardware coupled with the pervasive magnetic disturbances makes accurate heading estimation a challenging issue. Heading error is one of the main error sources of pedestrian dead reckoning. To reduce the heading error and enhance robustness, we proposed a novel heading estimation method based on Spatial Transformer Networks (STNs) and Long Short-Term Memory (LSTM), termed DeepHeading, which uses sensors embedded in a smartphone without any historical training data or dedicated infrastructure. We automatically annotate heading data based on map matching, and augment heading data based on device attitude. We leverage the STNs to align the device coordinate system and the navigation coordinate system, allow an unconstrained use of smartphones. Based on the characteristics of pedestrian heading continuity, we designed a hierarchical LSTM-basedSeq2Seq model to estimate the walking heading of the pedestrian. We conducted well-designed experiments to evaluate the performance of deepheading and compared it with the state-of-the-art heading estimation algorithms. The experimental results on real-world demonstrated that deepheading outperformed the compared heading estimation algorithms and achieved promising estimation accuracy with a median heading error of 4.52 • , mean heading error of 6.07 • and heading error of 9.18 • at the confidence of 80% when a pedestrian is walking in indoor environments with magnetic field disturbances. The proposed method is high-efficiency and easy to integrate with various mobile applications. INDEX TERMS Indoor positioning, heading estimation, pedestrian dead reckoning, deep learning.

show abstract

Online Path Description Learning Based on IMU Signals From IoT Devices

Zhuo,

Li,

et al. 2024

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Improving Pedestrian Dead Reckoning Using Likely Paths and Backtracking for Mobile Devices

Cited by 3 publications

References 17 publications

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM

Online Path Description Learning Based on IMU Signals From IoT Devices

Contact Info

Product

Resources

About