Indoor Positioning Using the OpenHPS Framework

Research paper presentation given at IPIN 2021, Lloret de Mar, Spain.

Hybrid positioning frameworks use various sensors and algorithms to enhance positioning through different types of fusion. The optimisation of the fusion process requires the testing of different algorithm parameters and optimal lowas well as high-level sensor fusion techniques. The presented OpenHPS open source hybrid positioning system is a modular framework managing individual nodes in a process network, which can be configured to support concrete positioning use cases or to adapt to specific technologies. This modularity allows developers to rapidly develop and optimise their positioning system while still providing them the flexibility to add their own algorithms. In this paper we discuss how a process network developed with OpenHPS can be used to realise a customisable indoor positioning solution with an offline and online stage, and how it can be adapted for high accuracy or low latency. For the demonstration and validation of our indoor positioning solution, we further compiled a publicly available dataset containing data from WLAN access points, BLE beacons as well as several trajectories that include IMU data.

Research paper: https://beatsigner.com/publications/indoor-positioning-using-the-openhps-framework.pdf

Transcript

1. Indoor Positioning Using the OpenHPS Framework Maxim Van de Wynckel, Beat Signer Web & Information Systems Engineering Lab Vrije Universiteit Brussel 1
2. What is OpenHPS? An Open Source Hybrid Positioning System 2
3. What is OpenHPS? An Open Source Hybrid Positioning System ► Any technology ► Any algorithm ► Various use cases ► Flexibile processing and output ⬛ Accuracy over battery consumption, reliability, ... ► Aimed towards ⬛ Developers ⬛ Researchers 3
4. Process Network Design IMU Sensor ... 4
5. Process Network Design ... IMU Sensor ... IMU Source PDR Processing Display Position ... ... Merge Frames 5
6. Process Network Design ... IMU Source PDR Processing Save Display Position Load ... ... Merge Frames Data Service 6
7. Modularity Core Component Positioning Techniques (e.g. fingerprinting) Abstractions (e.g. location-based services) Data Storage (e.g. MongoDB) Communication (e.g. socket connection) 7
8. Data Processing Processed Data Knowledge Raw Data 8
9. DataObject 9
10. Absolute and Relative Positions Absolute ► 2D, 3D, Geographical, ... Relative ► Distance, angle, velocity, ... ► Relative to another object 10
11. DataFrame VideoDataFrame source CameraObject uid: "camera", position: { x: 2, y: 5, z: 3 }, projection: ..., width: 1280, height: 1024 Image DataObject Detected object DataObject Detected object DataObject Detected object uid timestamp 11
12. SymbolicSpace An object that semantically defines a space ► Spatial hierarchy ► Graph connectivity with other spaces ► Geocoding ► GeoJSON compatibility ► Can be used as a location ► Can be extended ... 12
13. Location-based Service Location-based Service getCurrentPosition("me", ...) Sink Processing Fusion Storage Source Processing Source 13
14. Location-based Service ... Location-based Service watchPosition("me", ...) Sink Processing Fusion Storage Source Processing Source 14
15. Demonstration ► Indoor positioning use case ► Use existing techniques ► Validation of flexibility and modularity Server Offline App Online App 15
16. Positioning Model WiFi Source BLE Source Merge Frames Socket Sink User Input Offline-stage App 16
17. Positioning Model ... Socket Source Socket Source Fingerprint Storage Fingerprint Service Sink WLAN Fingerprin Processin BLE Fingerprin Processing Server Beacon Data Service 17
18. Positioning Model ... IMU Source Socket Source Position Fusion PDR Processing WiFi Source BLE Source Merge Frames Socket Sink Display Sink Online-stage App Velocity Processing Velocity Processing Delay 18
19. Positioning Model ... Socket Sink Socket Source Socket Source rint ge Fingerprint Service WLAN Fingerprint Processing BLE Multilateration Processing Position Fusion BLE Fingerprint Processing Server Beacon Data Service 19
20. Positioning Model ... IMU Source Socket Source Position Fusion PDR Processing WiFi Source BLE Source Merge Frames Socket Sink Display Sink Online-stage App Velocity Processing Velocity Processing Delay 20
21. Dataset 21
22. Validation Results Static Positioning WLAN fingerprinting BLE fingerprinting BLE multilateration Fusion failed points 0 6 12 0 average error 1.23 m 3.23 m 4.92 m 1.37 m minimum error 0.01 m 0.17 m 0.74 m 0.01 m maximum error 4.77 m 15.39 m 19.26 m 9.75 m hit rate 95.82 % 80.83 % 52.50 % 96.67 % 22
23. Validation Results ... Trajectories 23
24. Validation Results ... Trajectories WLAN + BLE WLAN + BLE + IMU average error 3.28 m 1.26 m maximum error 9.60 m 3.10 m average update frequency 3.04 s 0.52 s 24
25. Contributions and Conclusions ► OpenHPS: open source framework for hybrid positioning ⬛ Aimed towards developers and researchers ► Abstractions such as location-based services and spaces ► Validation of an indoor positioning use case ► Configurable and interchangeable nodes and services ► Public dataset with multiple orientations Visit for additional resources, documentation, source code and more! https://openhps.org 25