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To address the potential errors in the point cloud, we propose a calibration method based on projection and a novel rail extraction algorithm that effectively handles terrain variations and preserves the point cloud characteristics of the track area. We address the limitations of the traditional process involving fixed Euclidean thresholds by proposing a modulation function based on directional density variations to adjust the threshold dynamically. Finally, using PCA and local-ICP, we conduct feature analysis and classification of the clustered data to obtain the obstacle clusters. We conducted continuous experiments on the testing site, and the results showed that our system and algorithm achieved an STDR (stable detection rate) of over 95% for obstacles with a size of 15 cm \u00d7 15 cm \u00d7 15 cm in the range of \u00b125 m; at the same time, for obstacles of 10 cm \u00d7 10 cm \u00d7 10 cm, an STDR of over 80% was achieved within a range of \u00b120 m. This research provides a possible solution and approach for railway security via obstacle detection.<\/jats:p>","DOI":"10.3390\/s24103148","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T11:31:52Z","timestamp":1715772712000},"page":"3148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Novel High-Precision Railway Obstacle Detection Algorithm Based on 3D LiDAR"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0968-3720","authenticated-orcid":false,"given":"Zongliang","family":"Nan","sequence":"first","affiliation":[{"name":"Laboratory of All-Solid-State Light Sources, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 101407, China"}]},{"given":"Guoan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Laboratory of All-Solid-State Light Sources, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 101407, China"}]},{"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenghong (Taizhou) Laser Technology Co., Ltd., Taizhou 318001, China"}]},{"given":"Xuechun","family":"Lin","sequence":"additional","affiliation":[{"name":"Laboratory of All-Solid-State Light Sources, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 101407, China"}]},{"given":"Yingying","family":"Yang","sequence":"additional","affiliation":[{"name":"Laboratory of All-Solid-State Light Sources, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 101407, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Yan, F., Song, W., Wang, R., and Li, G. (2023). Automatic Obstacle Detection Method for the Train Based on Deep Learning. Sustainability, 15.","DOI":"10.3390\/su15021184"},{"key":"ref_2","first-page":"2687","article-title":"Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks","volume":"33","author":"Feng","year":"2022","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Qu, J., Li, S., Li, Y., and Liu, L. (2023). Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering. Electronics, 12.","DOI":"10.3390\/electronics12051175"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2286","DOI":"10.1109\/TITS.2020.3030496","article-title":"A Cross-Layer Defense Scheme for Edge Intelligence-Enabled CBTC Systems Against MitM Attacks","volume":"22","author":"Li","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1109\/LRA.2021.3124072","article-title":"FasterGICP: Acceptance-Rejection Sampling Based 3D Lidar Odometry","volume":"7","author":"Wang","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4531","DOI":"10.1109\/TITS.2020.3023189","article-title":"Airborne LiDAR Assisted Obstacle Recognition and Intrusion Detection Towards Unmanned Aerial Vehicle: Architecture, Modeling and Evaluation","volume":"22","author":"Miao","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12976","DOI":"10.1109\/JSEN.2022.3178179","article-title":"Probability of Unrecognized LiDAR Interference for TCSPC LiDAR","volume":"22","author":"Grollius","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhu, G., Nan, Z., Zhang, X., Chu, K., Zhan, S., Liu, X., and Lin, X. (2023). High anti-interference 3D imaging LIDAR system based on digital chaotic pulse position modulation. Opt. Laser Technol., 163.","DOI":"10.1016\/j.optlastec.2023.109405"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_10","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4\u20139). PointNet plus plus: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.3233\/JIFS-169706","article-title":"Design and implementation of a novel obstacle avoidance scheme based on combination of CNN-based deep learning method and liDAR-based image processing approach","volume":"35","author":"Zhou","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jiang, W., Chen, W., Song, C., Yan, Y., Zhang, Y., and Wang, S. (2023). Obstacle detection and tracking for intelligent agricultural machinery. Comput. Electr. Eng., 108.","DOI":"10.1016\/j.compeleceng.2023.108670"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1109\/LRA.2020.3047783","article-title":"PointMoSeg: Sparse Tensor-Based End-to-End Moving-Obstacle Segmentation in 3-D Lidar Point Clouds for Autonomous Driving","volume":"6","author":"Sun","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hata, A.Y., Osorio, F.S., and Wolf, D.F. (2014, January 8\u201311). Robust curb detection and vehicle localization in urban environments. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium Proceedings, Ypsilanti, MI, USA.","DOI":"10.1109\/IVS.2014.6856405"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.robot.2016.06.007","article-title":"3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes","volume":"83","author":"Asvadi","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Miao, Y., Li, S., Wang, L., Li, H., Qiu, R., and Zhang, M. (2023). A single plant segmentation method of maize point cloud based on Euclidean clustering and K-means clustering. Comput. Electron. Agric., 210.","DOI":"10.1016\/j.compag.2023.107951"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3330854","article-title":"KD-Tree-Based Euclidean Clustering for Tomographic SAR Point Cloud Extraction and Segmentation","volume":"20","author":"Guo","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1109\/JSTARS.2022.3141892","article-title":"Automatic Segmentation of Individual Grains from a Terrestrial Laser Scanning Point Cloud of a Mountain River Bed","volume":"15","author":"Walicka","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"25922","DOI":"10.1109\/JSEN.2021.3118365","article-title":"A Dynamic Clustering Algorithm for Lidar Obstacle Detection of Autonomous Driving System","volume":"21","author":"Gao","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, W., Song, C., Wang, H., Yu, M., and Yan, Y. (2023). Obstacle Detection by Autonomous Vehicles: An Adaptive Neighborhood Search Radius Clustering Approach. Machines, 11.","DOI":"10.3390\/machines11010054"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xie, D., Xu, Y., and Wang, R. (2019). Obstacle detection and tracking method for autonomous vehicle based on three-dimensional LiDAR. Int. J. Adv. Robot. Syst., 16.","DOI":"10.1177\/1729881419831587"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Moosmann, F., and Stiller, C. (2013, January 6\u201310). Joint self-localization and tracking of generic objects in 3D range data. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630716"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Che, E., Jung, J., and Olsen, M.J. (2019). Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review. Sensors, 19.","DOI":"10.3390\/s19040810"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"203","DOI":"10.5194\/isprs-archives-XLII-2-W7-203-2017","article-title":"Detection of Persons in MLS Point Clouds Using Implicit Shape Models","volume":"XLII-2\/W7","author":"Borgmann","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Burger, P., and Wuensche, H.J. (2018, January 26\u201330). Fast Multi-Pass 3D Point Segmentation Based on a Structured Mesh Graph for Ground Vehicles. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500552"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"24611","DOI":"10.1109\/ACCESS.2019.2898689","article-title":"A Point Cloud-Based Robust Road Curb Detection and Tracking Method","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhao, M., Lu, Y., Ran, Y., Tan, Z., and Luo, M. (2021). Design of 2D LiDAR and camera fusion system improved by differential evolutionary PID with nonlinear tracking compensator. Infrared Phys. Technol., 116.","DOI":"10.1016\/j.infrared.2021.103776"},{"key":"ref_28","first-page":"1","article-title":"An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC","volume":"19","author":"Dong","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1109\/JRFID.2022.3213852","article-title":"Fast Positioning Method of Truck Compartment Based on Plane Segmentation","volume":"6","author":"Zou","year":"2022","journal-title":"IEEE J. Radio Freq. Identif."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Anand, B., Senapati, M., Barsaiyan, V., and Rajalakshmi, P. (2021). LiDAR-INS\/GNSS-Based Real-Time Ground Removal, Segmentation, and Georeferencing Framework for Smart Transportation. IEEE Trans. Instrum. Meas., 70.","DOI":"10.1109\/TIM.2021.3117661"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4504","DOI":"10.1109\/TMM.2021.3119872","article-title":"Motion Estimation and Coding Structure for Inter-Prediction of LiDAR Point Cloud Geometry","volume":"24","author":"Li","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cao, Y., Wang, Y., Xue, Y., Zhang, H., and Lao, Y. (2022). FEC: Fast Euclidean Clustering for Point Cloud Segmentation. Drones, 6.","DOI":"10.3390\/drones6110325"},{"key":"ref_33","first-page":"205","article-title":"Selected Qualitative Aspects of Lidar Point Clouds: Geoslam Zeb-Revo and Faro Focus 3D X130","volume":"XLVIII-1\/W3","year":"2023","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s00371-019-01648-z","article-title":"An automatic 3D registration method for rock mass point clouds based on plane detection and polygon matching","volume":"36","author":"Hu","year":"2019","journal-title":"Vis. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Duan, Y., Yang, C., Chen, H., Yan, W., and Li, H. (2021). Low-complexity point cloud denoising for LiDAR by PCA-based dimension reduction. Opt. Commun., 482.","DOI":"10.1016\/j.optcom.2020.126567"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40747-021-00599-0","article-title":"Robust affine registration method using line\/surface normals and correntropy criterion","volume":"8","author":"Yilmaz","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/1719230","article-title":"Laser-Based Obstacle Detection at Railway Level Crossings","volume":"2016","author":"Amaral","year":"2016","journal-title":"J. Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, J., Li, R., Wang, J.Z., and Yan, M. (2019). Obstacle information detection method based on multiframe three-dimensional lidar point cloud fusion. Opt. Eng., 58.","DOI":"10.1117\/1.OE.58.11.116102"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhu, G., Nan, Z., Zhang, X., Yang, Y., Liu, X., and Lin, X. (2024). High precision rail surface obstacle detection algorithm based on 3D imaging LiDAR. Opt. Lasers Eng., 178.","DOI":"10.1016\/j.optlaseng.2024.108206"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/10\/3148\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:42:56Z","timestamp":1760107376000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/10\/3148"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,15]]},"references-count":39,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24103148"],"URL":"https:\/\/doi.org\/10.3390\/s24103148","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,15]]}}}