Mapless Indoor Navigation Based on Landmarks

Gao, Lulu; Konomi, Shin’ichi

doi:10.1007/978-3-031-05431-0_4

Cited by 1 publication

(2 citation statements)

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“…Spatial contexts, such as landmarks, can be properly chosen to calibrate the localization error based on the inherent spatial information without additional deployment costs. Landmark is defined as a spatial point with salient features and semantic characteristics from its near environment in indoor positioning systems, such as corners, stairs, and elevators [ 27 ]. These features can be observed for identification in one or a combination of different sensors as people pass through the landmark.…”

Section: Methodsmentioning

confidence: 99%

“…The duration is captured from the Wi-Fi network logs and the distance is calculated by the PDR positioning trajectory, calibrated by recognized landmarks with the help of a CNN, ensuring the performance of contact tracing. Although integration with the existing infrastructure is beneficial in mitigating the deployment costs, it may not fully satisfy the requirements of contact tracing with the high spatiotemporal resolution because of the absent coverage [ 27 ]. The trajectory obtained by the PDR technique, without requiring special infrastructure, can improve the coarse-grained duration and make it fine-grained.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%