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We propose an accessible indoor navigation application. The solution integrates information of floor plans, Bluetooth beacons, Wi-Fi/cellular data connectivity, 2D/3D visual models, and user preferences. Hybrid models of interiors are created in a modeling stage with Wi-Fi/ cellular data connectivity, beacon signal strength, and a 3D spatial model. This data is collected, as the modeler walks through the building, and is mapped to the floor plan. Client-server architecture allows scaling to large areas by lazy-loading models according to beacon signals and/or adjacent region proximity. During the navigation stage, a user with the designed mobile app is localized within the floor plan, using visual, connectivity, and user preference data, along an optimal route to their destination. User interfaces for both modeling and navigation use visual, audio, and haptic feedback for targeted users. While the current pandemic event precludes our user study, we describe its design and preliminary results.
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Enhancing indoor smartphone location acquisition using floor plans
Indoor localization systems typically determine a position using either ranging measurements, inertial sensors, environmental-specific signatures or some combination of all of these methods. Given a floor plan, inertial and signature-based systems can converge on accurate locations by slowly pruning away inconsistent states as a user walks through the space. In contrast, range-based systems are capable of instantly acquiring locations, but they rely on densely deployed beacons and suffer from inaccurate range measurements given non-line-of-sight (NLOS) signals. In order to get the best of both worlds, we present an approach that systematically exploits the geometry information derived from building floor plans to directly improve location acquisition in range-based systems. Our solving approach can disambiguate multiple feasible locations taking into account a mix of LOS and NLOS hypotheses to accurately localize with significantly fewer beacons. We demonstrate our geometry-aware solving approach using a new ultrasonic beacon platform that is able to perform direct time-of-flight ranges on commodity smartphones. The platform uses Bluetooth Low Energy (BLE) for time synchronization and ultrasound for measuring propagation distance. We evaluate our system's accuracy with multiple deployments in a university campus and show that our approach shifts the 80% accuracy point from 4 -- 8m to 1m as compared to solvers that do not use the floor plan information. We are able to detect and remove NLOS signals with 91.5% accuracy.
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- Award ID(s):
- 1722173
- PAR ID:
- 10075313
- Date Published:
- Journal Name:
- International Conference on Information Processing in Sensor Networks
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPSN.2018.00056
