Publications
Journals
Feng Huang, W. Wen, J. Zhang and L. -T. Hsu, “Point Wise or Feature Wise? A Benchmark Comparison of Publicly Available Lidar Odometry Algorithms in Urban Canyons,” in IEEE Intelligent Transportation Systems Magazine, doi: 10.1109/MITS.2021.3092731. (paper)
Feng Huang, Weisong Wen, J. Zhang, Chaoqun Wang and Li-Ta Hsu. Dynamic Object-aware LiDAR Odometry Aided by Weightings Optimization in Dense Urban Areas, in IEEE Transactions on Intelligent Vehicles, doi: 10.1109/TIV.2023.3338141. (paper, Project)
F. Huang, W. Wen, G. Zhang, D. Su and Y. Huang, “Continuous Error Map Aided Adaptive Multi-Sensor Integration for Connected Autonomous Vehicles in Urban Scenarios,” in IEEE Transactions on Instrumentation and Measurement, doi: 10.1109/TIM.2025.3573351.(paper, Project)
Hsu, L-T., Huang, F., Ng, H-F., Zhang, G., Zhong, Y., Bai, X., & Wen, W. (2023). Hong Kong UrbanNav: An open-source multisensory dataset for benchmarking urban navigation algorithms. NAVIGATION, 70(4). https://doi.org/10.33012/navi.602
Chen, P., Guan, W., Huang, F., Zhong, Y., Wen, W., Hsu, L. T., & Lu, P. (2023). ECMD: An Event-Centric Multisensory Driving Dataset for SLAM. IEEE Transactions on Intelligent Vehicles.(paper,Project)
Zhang J, Wen W, Huang F, Wang Y, Chen X, Hsu L-T. GNSS-RTK Adaptively Integrated with LiDAR/IMU Odometry for Continuously Global Positioning in Urban Canyons. Applied Sciences. 2022; 12(10):5193. https://doi.org/10.3390/app12105193
Zhang J, Wen W, Huang F, Chen X, Hsu L-T. Coarse-to-Fine Loosely-Coupled LiDAR-Inertial Odometry for Urban Positioning and Mapping. Remote Sensing. 2021; 13(12):2371. https://doi.org/10.3390/rs13122371
J. Chang, L. Zhang, L. -T. Hsu, B. Xu, F. Huang and D. Xu, “Analytic Models of a Loosely-coupled GNSS/INS/LiDAR Kalman Filter considering Update Frequency under a Spoofing Attack,” in IEEE Sensors Journal, 2022, doi: 10.1109/JSEN.2022.3212977.
Zhong, Y., Huang, F., Zhang, J., Wen, W., Hsu, L.-T.: Low-cost solid-state LiDAR/inertial-based localization with prior map for autonomous systems in urban scenarios. IET Intell. Transp. Syst. 00, 1– 13 (2022). https://doi.org/10.1049/itr2.12273
J. Chang, F. Huang, L. Zhang, D. Xu and L. -T. Hsu, “Selection of Areas for Effective GNSS Spoofing Attacks to a Vehicle-mounted MSF System based on Scenario Classification Models,” in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2023.3279831.
J. Chang, R. Hu, F. Huang*, D. Xu and L. -T. Hsu, “LiDAR-Based NDT Matching Performance Evaluation for Positioning in Adverse Weather Conditions,” in IEEE Sensors Journal, vol. 23, no. 20, pp. 25346-25355, 15 Oct.15, 2023, doi: 10.1109/JSEN.2023.3312911.
J. Chang, Y. Zhang, S. Fan, F. Huang, D. Xu and L. -T. Hsu, “An Anti-spoofing Model based on MVM and MCCM for a Loosely-coupled GNSS/INS/LiDAR Kalman Filter,” in IEEE Transactions on Intelligent Vehicles, doi: 10.1109/TIV.2023.3335356.
Conference Proceedings
Huang, F., Zhong, Y., Chen, H., Su, D., Wu, J., Wen, W., & Hsu, L. T.. Roadside GNSS Aided Multi-Sensor Integrated System for Vehicle Positioning in Urban Areas. Accepted by IEEE IROS 2025. (Project)
Qin, Q., Zhang, Z., Zhong, Y., Huang, F., Liu, X., Hu, R., Chen, H., Hu, W., Su, D., Zhang, J., Ng, H.-F., & Wen, W. (2025). UrbanV2X: A Multisensory Vehicle-Infrastructure Dataset for Cooperative Navigation in Urban Areas， Accepted by IEEE ITSC 2025. （Project）
Yan, Penggao, Wen, Weisong, Huang, Feng, Hsu, Li-Ta, “A Fault Detection Algorithm for LiDAR/IMU Integrated Localization Systems with Non-Gaussian Noises,” Proceedings of the 2024 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2024, pp. 561-574. https://doi.org/10.33012/2024.19564
F. Huang, H. Chen, A. Urtay, D. Su, W. Wen and L. -T. Hsu, “Roadside Infrastructure assisted LiDAR/Inertial-based Mapping for Intelligent Vehicles in Urban Areas,” 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 5831-5837, doi: 10.1109/ITSC57777.2023.10422552. (Paper, Project, Video)
F. Huang, W. Wen, G. Zhang, D. Su and L. -T. Hsu, “Adaptive Multi-Sensor Integrated Navigation System Aided by Continuous Error Map from RSU for Autonomous Vehicles in Urban Areas,” 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 5895-5902, doi: 10.1109/ITSC57777.2023.10422216. (Paper, Project, Video)
Sun, Y., Huang, F., Wen, W., Hsu, L.-T., and Liu, X.: MULTI-ROBOT COOPERATIVE LIDAR SLAM FOR EFFICIENT MAPPING IN URBAN SCENES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1/W1-2023, 473–478, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-473-2023, 2023.
Feng Huang, Weisong Wen, Hoi-Fung Ng, Li-Ta Hsu, “LiDAR Aided Cycle Slip Detection for GNSS Real-time Kinematic Positioning in Urban Environments,” 2022 IEEE International Intelligent Transportation Systems Conference (ITSC), 2022.doi: 10.1109/ITSC55140.2022.9921814. (Paper, Video)
Huang, Feng, Shen, Donghui, Wen, Weisong, Zhang, Jiachen, Hsu, Li-Ta, “A Coarse-to-Fine LiDAR-Based SLAM with Dynamic Object Removal in Dense Urban Areas,” Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 3162-3172.https://doi.org/10.33012/2021.18083 (Paper, Video)
Zhang, Jiachen, Wen, Weisong, Huang, Feng, Chen, Xiaodong, Hsu, Li-Ta, “Continuous GNSS-RTK Aided by LiDAR/Inertial Odometry with Intelligent GNSS Selection in Urban Canyons,” Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 4198-4207.
PhD Thesis
Dynamic Object-Aware LiDAR Odometry In Urban Areas: From Single To Cooperative Navigation
HUANG Feng
Thesis Committee:
Prof. LIU Wei , Prof. KONG Hui and Prof. Gu Shengfeng
This thesis addresses robust and precise positioning for autonomous systems in urban environments by tackling challenges in LiDAR odometry and GNSS-based navigation. The main contributions are: (1) evaluating LiDAR odometry in dynamic urban canyons and identifying key factors affecting performance; (2) developing learning-based and optimization-based methods to mitigate dynamic object and outlier impacts in LiDAR odometry; and (3) integrating roadside infrastructure, such as LiDAR and GNSS, into multi-sensor frameworks for enhanced positioning accuracy. These advances improve reliable navigation for autonomous driving in challenging urban areas.