3D LiDAR Aided GNSS Positioning

Our Approach

1. Perceive the signal environment

   Real-time 3D LiDAR and camera perception identifies the buildings and vehicles that block or reflect each satellite signal.

2. Model and correct NLOS and multipath

   Physical and learned models correct biased pseudoranges or exclude them, from single-point positioning to RTK.

3. Improve satellite geometry

   LiDAR landmarks act as 'virtual satellites', and 5G and LEO signals add constraints where blocked satellites leave poor geometry.

4. Robust estimation with factor graphs

   Robust losses, multi-epoch ambiguity resolution and integrity constraints keep the solution reliable in deep canyons.

Research in Action

Systems we have built and tested with partners. Open a card for the problem, our approach, results and photos.

LiDAR-Aided GNSS NLOS Modelling

Modelling and mitigating multipath and NLOS signals with 3D LiDAR for urban robots.

• 12–20% accuracy gain
• KLT open NLOS dataset

Problem, approach & results

ProblemReflected and blocked GNSS signals cause metre-level errors in cities, and existing models ignore the real 3D surroundings.

Approach

• Real-time LiDAR perception of buildings and vehicles that block or reflect signals
• Physical NLOS/multipath models driven by the LiDAR scene
• Correction or exclusion inside GNSS/INS factor graphs

3D LiDAR-Aided GNSS Positioning in Urban Canyons

3D LiDAR used to detect and correct NLOS satellites so that GNSS-RTK keeps its fix in urban canyons.

• GLIO IEEE T-IV 2023
• RTK higher fix rate

Problem, approach & results

ProblemGNSS-RTK loses its fixed solution in urban canyons because NLOS satellites are not detected.

Approach

• 3D LiDAR point clouds detect buildings that block satellites
• NLOS exclusion and correction before RTK
• Factor-graph integration of GNSS, LiDAR and IMU

ResultsGLIO and dynamic-object-aware LiDAR odometry (IEEE T-IV); geospatial factor-graph positioning (IEEE TIM).

Lane-Level Localization with Raw GNSS, LiDAR and IMU

Raw GNSS, LiDAR and IMU tightly fused for lane-level positioning in urban canyons, with Huawei.

• TechConnect innovation award 2021
• T-ITS NLOS paper 2022
• US patent 12,123,961 granted

Problem, approach & results

ProblemGNSS-RTK fixes are lost in urban canyons, and LiDAR odometry drifts among moving objects without a prior map.

Approach

• Raw GNSS, LiDAR and IMU tightly coupled in one optimisation
• 3D-LiDAR-based NLOS exclusion and cycle-slip detection
• LiDAR landmarks used as “virtual satellites” to raise the RTK fix rate

ResultsPapers in IEEE T-ITS, Journal of Geodesy, IET ITS, ITSC 2022 and ION GNSS+ 2021; US patent 12,123,961 on 3D LiDAR-aided GNSS.

Vision-Aided GNSS-RTK Positioning for UAVs in Urban Canyons

Sky-view NLOS detection with a fisheye camera so delivery drones keep RTK-level positioning in deep street canyons.

• >99% NLOS detection accuracy
• ITSC 2023 paper

Problem, approach & results

ProblemDrone delivery needs RTK-level positions, but reflected signals in deep canyons stop RTK from fixing.

Approach

• Fisheye sky segmentation (Swin Transformer) flags NLOS satellites
• Learned pseudorange-bias correction tightly coupled with GNSS
• RTK ambiguity resolution with NLOS exclusion

ResultsMarsTalk 2025 industry salon with Meituan at PolyU and a PolyU–Meituan press release.

GNSS/5G Integrated Positioning for UAVs

GNSS and 5G fused with quantified integrity so that drones can fly safely through complex urban scenes.

• Safety certifiable positioning output

Problem, approach & results

ProblemUrban UAV routes pass through deep street canyons where GNSS alone is biased by NLOS signals and gives no safety bound.

Approach

• NLOS-aware GNSS positioning aided by 5G ranging, IMU, LiDAR and cameras
• Protection levels that certify when the position can be trusted for navigation
• Requirement analysis, algorithm design and flight validation in Hong Kong scenes

Data-Driven-Assisted GNSS RTK/INS Navigation in Urban Canyons

Deep networks that clean raw GNSS and IMU data for RTK/INS navigation.

• 12–20% accuracy gain
• 0.5 s per epoch on Jetson

Problem, approach & results

ProblemUrban GNSS errors are strongly correlated in time and space, which hand-made models cannot capture.

Approach

• Deep networks that detect and mitigate GNSS outliers
• Spatio-temporal IMU noise model
• Multi-epoch features to detect wrong RTK fixes

ResultsPreliminary results attracted the Meituan UAV collaboration; featured by InsideGNSS on AI for GNSS.

GNSS/IMU/Camera Factor-Graph Vehicle Positioning in Urban Environments

Multi-epoch ambiguity resolution in a factor graph for lane-level in-car navigation.

• ≈5% extra accuracy gain
• ION GNSS+ 2024 paper

Problem, approach & results

ProblemCar navigation apps need lane-level accuracy in cities, but single-epoch GNSS cannot resolve carrier-phase ambiguities there.

Approach

• Factor-graph optimisation of GNSS, IMU and camera over a sliding window
• Multi-epoch carrier-phase ambiguity resolution
• Robust weighting against outliers and map-matching checks

ResultsION webinar “FGO for GNSS/INS integration” and an ION GNSS+ 2024 paper.

Perception-based PPP-RTK/LVINS

Perception-aided precise positioning: PPP-RTK fused with LiDAR-visual-inertial navigation.

• PPP-RTK + LVINS

Problem, approach & results

ProblemPPP-RTK gives centimetre accuracy in open sky but collapses in cities, where vision and LiDAR could help.

Approach

• PPP-RTK corrections fused with LiDAR-visual-inertial navigation (LVINS)
• Perception used to detect NLOS satellites and keep ambiguities fixed
• Tests on ground vehicles and UAVs with Hong Kong and Tokyo datasets

Resilient GNSS Positioning for UAVs

Robust factor-graph GNSS positioning for UAVs, the foundation of the lab's urban-positioning research.

• FGO-GNC robust estimator
• GMM noise model

Problem, approach & results

ProblemUAVs in cities face non-Gaussian GNSS outliers, so standard estimators break where positioning matters most.

Approach

• Factor-graph GNSS positioning with pseudorange and Doppler over time
• Adaptive weighting with a Geman–McClure loss solved by graduated non-convexity (FGO-GNC)
• Gaussian-mixture noise models and integration with 3D LiDAR

ResultsThe methods underpin the lab's later urban-positioning work with Huawei, Meituan and Tencent.

LEO-Satellite V2X and Connected Autonomous Vehicles Survey

A survey and theoretical model of Starlink-class LEO satellites for urban vehicle positioning.

• FGO best accuracy
• Starlink TLE-based model

Problem, approach & results

ProblemGNSS alone cannot give connected vehicles reliable positions in Hong Kong's canyons; LEO constellations add many fast-moving signals.

Approach

• Survey of LEO-satellite-enabled V2X and CAV systems and key index criteria
• Simulated Starlink measurements from TLE files fused with real GNSS data in Whampoa
• Comparison of GNSS-only, GNSS+LEO and factor-graph (GNSS+LEO) positioning

Video Demonstrations

Recognition & Media

• Best Presentation Award, ION GNSS+ 2020, session on Navigation in Urban Environments
• Innovation Award, TechConnect
• Featured by Inside GNSS on perceived-environment-aided GNSS positioning

Selected Publications

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 →