3D Map-Aided GNSS (3DMA) for Smartphone and IoT

This program uses 3D city models, skymask matching, and lightweight ray tracing to improve GNSS positioning and reliability assessment on smartphone-grade and IoT-class receivers — the consumer devices where positioning is most likely to fail because they cannot afford rich onboard sensing. The urban environment is not only an obstacle; it can become prior information for signal prediction, NLOS detection, multipath modeling, and integrity-aware positioning at the constraints of mobile hardware.

Why it matters. Most GNSS users worldwide carry a single, low-cost receiver in their pocket. The smartphone is the dominant urban-positioning platform, and standalone smartphone GNSS in dense cities is unreliable. 3DMA GNSS turns a known 3D city model into a measurement aid usable within smartphone compute, power, and memory budgets — and is the foundation of the lab’s longest-running industry partnership.

Funding.

• Huawei — long-running collaborative research partnership on 3DMA GNSS, deep integration, indoor positioning, collaborative GNSS, and multi-sensor fusion for mobile devices (since 2018; 9+ contracts under the framework)
• ITF-ITSP — Improving Smartphone Urban Positioning aided by AI for HK Smart Cities, HK$801k (PI, 2024)

Industry engagement — Google Android Location. Prof. Hsu served as Visiting Research Scientist at Google (Mountain View, USA), on the Android Location team for 20 months (2022–2023), working on FGO-based smartphone positioning. The collaboration continued with a Pixel team talk in August 2024 and ongoing exchange between IPNL research and the Android Location group on factor-graph approaches to smartphone GNSS.

Other industry partners. Huawei Car BU, OPPO, AIROHA (Taiwan), MediaTek (Taiwan), NYU Langone Medical Center (3DMA GNSS for vision-impaired pedestrians, since 2021).

Recognition. Founding chair, ION/IAG Working Group on Positioning at Asian Urban Canyons — the international community vehicle for smartphone-grade urban GNSS, originating from Prof. Hsu’s postdoc work in Tokyo on 3DMA GNSS.

Representative publications. 3D-mapping-aided PPP-RTK aiming at deep urban canyons (Xin et al., Journal of Geodesy 2022); 3D Mapping Database Aided GNSS RTK (Ng, Hsu, IEEE TAES 2021); Grid-based 3DMA GNSS with clustering and Doppler velocity using FGO (Ng et al., Journal of Navigation 2025); FGO-Based Smartphone IMU-Only Indoor SLAM (Bai, Wen, Su, Hsu, IEEE TMC 2024); Adaptive Weighted GNSS/VINS/Wi-Fi RTT-based Seamless Positioning System for Smartphone (Su et al., IPIN 2024); Sky Visibility Estimation Using Satellite Signals for Building Update Detection (Xu Haosheng et al.).

Related project — for higher-cost autonomous platforms (AVs, UAVs, robots) with rich onboard sensors: Environment-Aware GNSS for UAS and Autonomous Driving.