3D LiDAR Aided GNSS Positioning

Positioning in urban environments is becoming essential due to the increasing demand for autonomous driving vehicles (ADV). The global navigation satellite system (GNSS) is currently one of the principal means of providing globally-referenced positioning for ADV localization. With the increased availability of multiple satellite constellations, GNSS can provide satisfactory performance in open-sky areas. However, the positioning accuracy is significantly degraded in highly-urbanized cities such as Hong Kong, due to signal reflection caused by static buildings and dynamic objects such as double-decker buses. If the direct line-of-sight (LOS) is blocked, and reflected signals from the same satellite are received, the notorious non-line-of-sight (NLOS) receptions occur. According to a recent review paper, NLOS is currently the major difficulty in the use of GNSS in intelligent transportation systems.

Inspired by the strong perception capability of ADV using onboard sensors (such as 3D LiDAR), we continuously developed the perception-aided NLOS mitigation methods where the 3D LiDAR is employed to timely reconstruct the surrounding environments to identify the NLOS receptions. The idea was also reported in the industrial magazine in 2018. The work was further improved in 2020, where several drawbacks are relaxed and was awarded the Best Presentation Award in the session of Navigation in Urban Environments. Interestingly, this award is selected by the session chairs from Waymo and Swift Navigation. Meanwhile, the idea is transferred into industrial applications for high-accuracy offline mapping applications. Recently, we extended the LiDAR aided GNSS NLOS mitigation to the GNSS Real-time Kinematic (RTK), leading to sub-meter level accuracy. Unfortunately, the fixed rate of the RTK is still not guaranteed as:

The existing method does not fully mitigate the NLOS with multiple reflections and multipath. It is still an unknown question to model the multiple reflection and multipath.
Poor satellite geometry due to the signal blockage and potential NLOS exclusion. It is still an unknown question to effectively improve the geometry of the satellite constraints in dense urban canyons.

3D LiDAR Aided GNSS Positioning for Robotics Urban Navigation

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Recent News

May 2022, 1 paper accepted in IEEE Transactions on Intelligent Transportation Systems.
- Wen, W., and Hsu, L.T., 2022. 3D LiDAR Aided GNSS NLOS Mitigation in Urban Canyons. IEEE Transactions on Intelligent Transportation Systems.
23rd April 2022, our conference paper was accepted in ION GNSS+ 2022.
- Liu, X., Wen, W*., and Hsu, L.T. 2022, September. 3D LiDAR Aided GNSS Real-time Kinematic Positioning via Coarse-to-fine Batch Optimization for High Accuracy Mapping in Dense Urban Canyons. In Proceedings of ION GNSS+ 2022.

Video Demonstration

Demonstration: 3D LiDAR Aided NLOS Exclusion for GNSS Real-time Kinematic (RTK) Positioning in Urban Canyons

Presentation in ION GNSS+ 2021: 3D LiDAR Aided NLOS Exclusion for GNSS RTK Positioning

Demonstration: 3D LiDAR Aided NLOS Exclusion for GNSS Single Point Positioning

Presentation in ION GNSS+ 2020: 3D LiDAR Aided GNSS and Its Tightly Coupled Integration with INS

Presentation in ION GNSS+ 2021: Continuous GNSS-RTK Aided by LiDAR/Inertial Odometry

2025

3-D LiDAR-Aided GNSS NLOS Mitigation for Reliable GNSS-RTK Positioning in Urban Canyons.
Liu, X., Wen, W.*, Huang, F., Gao, H., Wang, Y., Hsu, L.T.
IEEE Transactions on Instrumentation and Measurement, 74, 1-15, 2025. (IF: 5.9, JCR Q1, Citations: 9)
3D LiDAR Aided GNSS NLOS Correction by Direction-of-Arrival Estimation Using Doppler Measurements in Urban Canyons.
Liu, X., Wen, W.*, Zhang, L., Hsu, L.T.
IEEE Transactions on Intelligent Transportation Systems, 2025. (IF: 8.4, JCR Q1)
Fisheye Image/GNSS Based Multimodal Learning for GNSS NLOS/Multipath Correction: Enhancing Vehicle Positioning in Urban Canyons for Autonomous Driving.
Hu, R., Liu, J., Zhong, Y., Wen, W., Xia, M., Huang, Y.
IEEE Transactions on Vehicular Technology, 2025. (IF: 7.1, JCR Q1)
pyrtklib: An Open-source Package for Tightly Coupled Deep Learning and GNSS Integration for Positioning in Urban Canyons.
Hu, R., Xu, P., Zhong, Y., Wen, W.
IEEE Transactions on Intelligent Transportation Systems, 2025. (IF: 8.4, JCR Q1, Citations: 7)
Urban GNSS Positioning for Consumer Electronics: 3D Mapping and Advanced Signal Processing.
Wang, J., Xia, M., Zhang, D., Wen, W., Chen, W., Shi, C.
IEEE Transactions on Consumer Electronics, 2025. (IF: 10.9, JCR Q1, Citations: 7)

2024

Integrity-Constrained Factor Graph Optimization for GNSS Positioning in Urban Canyons.
Xia, X., Wen, W., Hsu, L.T.*
NAVIGATION: Journal of the Institute of Navigation, 2024. (IF: 3.1, JCR Q1, Citations: 5)
Subspace-based Adaptive GMM Error Modeling for Fault-Aware Pseudorange-based Positioning in Urban Canyons.
Yan, P., Xia, X., Brizzi, M., Wen, W., Hsu, L.T.*
IEEE Transactions on Intelligent Vehicles, 2024. (IF: 14.3, JCR Q1, Citations: 4)
Trajectory Smoothing Using GNSS/PDR Integration Via Factor Graph Optimization in Urban Canyons.
Zhong, Y., Wen, W.*, Hsu, L.T.
IEEE Internet of Things Journal, 2024. (IF: 8.9, JCR Q1, Citations: 16)
Enhancing GNSS Positioning Accuracy for Road Monitoring Systems: A Factor Graph Optimization Approach Aided by Geospatial Information.
Zhong, Y., Hu, R., Bai, X., Li, X., Hsu, L.T., Wen, W.
IEEE Transactions on Instrumentation and Measurement, 73, 1-12, 2024. (IF: 5.9, JCR Q1, Citations: 12)

2023

3D Vision Aided GNSS Real-time Kinematic Positioning for Autonomous Systems in Urban Canyons.
Wen, W.*, Bai, X., Hsu, L.T.
NAVIGATION: Journal of the Institute of Navigation, 2023. (IF: 3.1, JCR Q1, Citations: 22)
Hong Kong UrbanNav: An Open-Source Multisensory Dataset for Benchmarking Urban Navigation Algorithms.
Hsu, L.T., Huang, F., Ng, H.F., Zhang, G., Zhong, Y., Bai, X., Wen, W.
NAVIGATION: Journal of the Institute of Navigation, 70(4), 2023. (IF: 3.1, JCR Q1, Citations: 80)
GLIO: Tightly-coupled GNSS/LiDAR/IMU Integration for Continuous and Drift-free State Estimation of Intelligent Vehicles in Urban Areas.
Liu, X., Wen, W.*, Hsu, L.T.
IEEE Transactions on Intelligent Vehicles, 2023. (IF: 14.3, JCR Q1, Citations: 52)

2021–2022

3D LiDAR Aided GNSS NLOS Mitigation in Urban Canyons.
Wen, W., and Hsu, L.T.
IEEE Transactions on Intelligent Transportation Systems, 2021.
Time-correlated Window Carrier-phase Aided GNSS Positioning in Urban Canyons.
Bai, X., Wen, W.*, Hsu, L.T.
IEEE Transactions on Aerospace and Electronic Systems, 2022.
GNSS Outlier Mitigation via Graduated Non-convexity Factor Graph Optimization.
Wen, W., Zhang, G., Hsu, L.T.
IEEE Transactions on Vehicular Technology, 71(1), 297-310, 2021.
Factor Graph Optimization for GNSS/INS Integration: A Comparison with the Extended Kalman Filter.
Wen, W., Pfeifer, T., Bai, X., Hsu, L.T.
NAVIGATION: Journal of the Institute of Navigation, 68(2), 315-331, 2021.

2018–2020

Object-Detection-Aided GNSS and Its Integration With Lidar in Highly Urbanized Areas.
Wen, W., Zhang, G., Hsu, L.T.
IEEE Intelligent Transportation Systems Magazine, 12(3), 53-69, 2020.
GNSS NLOS Exclusion Based on Dynamic Object Detection Using LiDAR Point Cloud.
Wen, W., Zhang, G., Hsu, L.T.
IEEE Transactions on Intelligent Transportation Systems, 2019.
Correcting NLOS by 3D LiDAR and Building Height to Improve GNSS Single Point Positioning.
Wen, W., Zhang, G., Hsu, L.T.
NAVIGATION: Journal of the Institute of Navigation, 66(4), 705-718, 2019.
Tightly Coupled GNSS/INS Integration via Factor Graph and Aided by Fish-eye Camera.
Wen, W., Bai, X., Kan, Y.C., Hsu, L.T.
IEEE Transactions on Vehicular Technology, 68(11), 10651-10662, 2019.

→ Full publication list

Press Coverage

Inside GNSS — Feature on perceived environment aided GNSS positioning
Innovation Award from TechConnect
Best Presentation Award in ION GNSS+ 2020 — Session on Navigation in Urban Environments

Acknowledgement and Collaborators

This research was funded by government and industry partners, including Hong Kong Polytechnic University, Guangdong Basic and Applied Basic Research Foundation, Riemann Laboratory, and Huawei Technologies.

Funding and Collaborators