Pillar lead: Weisong Wen
Factor graph optimization is the mathematical engine that links signals, measurements, maps, sensors, and integrity. We use it as a unifying framework — letting navigation systems reason over time, fuse heterogeneous measurements, and handle outliers more flexibly than classical filtering-only pipelines.
The foundational paper Factor Graph Optimization for GNSS/INS Integration: A Comparison with the Extended Kalman Filter (Wen, Pfeifer, Bai, Hsu, NAVIGATION 2021) was named the 2024 Most-Cited Paper in NAVIGATION: Journal of the Institute of Navigation. The follow-on work extends FGO into integrity-constrained estimation, smartphone IMU-only indoor SLAM (IEEE TAES 2024), 3D vision-aided GNSS RTK (NAVIGATION 2023), and self-supervised weighting (AutoW, ION GNSS+ 2024). The open-source GraphGNSSLib library is the community-facing artifact of this pillar.
Lab Contributors
Related Projects
- Multi-Agent Collaborative GNSS for Intelligent Transportation Systems completed
- UrbanNav active
- LunaNav: Lunar Robotic Exploration active
- Robust GNSS Navigation for Urban Air Traffic Management active
- GraphGNSSLib active
- Universal Vehicle Self-Diagnosis: Predictive Component Monitoring active
- 3D Map-Aided GNSS (3DMA) for Smartphone and IoT active
- Environment-Aware GNSS for UAS and Autonomous Driving active
- Precision Start-Gate Monitoring for Olympic-Class Windsurfing active