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Semantic VPS for Smartphone Localization in Challenging Urban Environments
Max Jwo Lem Lee1, Li-Ta Hsu1,2, Hoi-Fung Ng1
1Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong.
Sensors (Basel, Switzerland)
|September 28, 2021
Summary
This study introduces a semantic Visual Positioning System (VPS) for precise smartphone localization in urban canyons, outperforming existing methods. The system leverages building information modeling (BIM) and computer vision for robust outdoor positioning where GPS fails.
Area of Science:
- Computer Vision
- Geomatics Engineering
- Internet of Things (IoT)
Background:
- Global Navigation Satellite System (GNSS) performance degrades in urban canyons.
- Smart city development increases the availability of Building Information Modeling (BIM).
- Accurate outdoor localization is crucial for emerging IoT applications.
Purpose of the Study:
- To present a novel semantic Visual Positioning System (VPS) for accurate outdoor localization.
- To address the limitations of GNSS in deep urban canyons.
- To improve position and orientation estimation using BIM and computer vision.
Main Methods:
- Offline stage: Material-segmented BIM used to generate segmented reference images.
- Online stage: Smartphone images segmented using computer vision algorithms.
- Semantic VPS matches segmented images to estimate position (latitude, longitude, altitude) and orientation (yaw, pitch, roll).
Main Results:
- Achieved positioning accuracy of 2.0 m (high-rise), 5.5 m (foliage), and 15.7 m (alleyway).
- Demonstrated a 45% improvement in positioning accuracy over state-of-the-art methods.
- Obtained yaw estimation accuracy of 2.3°, an eight-fold improvement over smartphone IMU.
Conclusions:
- The semantic VPS offers accurate and robust localization in challenging urban environments.
- This method significantly enhances positioning capabilities where GNSS is unreliable.
- The system provides a viable solution for precise navigation in smart city IoT applications.
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