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Real-Time Loosely Coupled 3DMA GNSS/Doppler Measurements Integration Using a Graph Optimization and Its Performance
Hoi-Fung Ng1, Li-Ta Hsu1, Max Jwo Lem Lee1
1Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.
Sensors (Basel, Switzerland)
|September 9, 2022
Summary
Precise localization for people with blindness and low vision (BLV) is improved using a novel 3D mapping-aided (3DMA) Global Navigation Satellite System (GNSS) system. This advanced wearable technology enhances safe navigation in challenging urban environments.
Area of Science:
- Engineering
- Computer Science
- Geomatics
Background:
- Smart health applications offer solutions for individuals with disabilities.
- People with blindness and low vision (BLV) face challenges in environmental perception and navigation.
- Accurate localization is crucial for BLV navigation but is limited in urban canyons by conventional Global Navigation Satellite System (GNSS) methods.
Purpose of the Study:
- To develop and evaluate a real-time 3D mapping-aided (3DMA) GNSS positioning system for BLV individuals.
- To address the limitations of conventional GNSS in urban environments for precise localization.
- To enhance the robustness and accuracy of wearable navigation systems for BLV.
Main Methods:
- Developed a real-time 3DMA GNSS positioning system integrating shadow matching with likelihood-based ranging.
- Optimized the 3DMA GNSS solution using Doppler measurements and factor graph optimization (FGO) in a loosely-coupled manner.
- Evaluated system performance using data from an advanced wearable system in New York City.
Main Results:
- The real-time forward-processed FGO achieved a root-mean-square error (RMSE) of approximately 21 meters.
- Post-processing the data with FGO in a combined direction reduced the RMSE to 16 meters.
- The proposed loosely-coupled 3DMA FGO algorithm demonstrated improved and robust positioning performance.
Conclusions:
- The developed 3DMA GNSS system offers a viable solution for precise localization in urban environments for BLV individuals.
- Loosely-coupled factor graph optimization enhances the robustness of multi-sensor integration for wearable navigation.
- The system shows significant potential for improving the independence and safety of persons with BLV.
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