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the Chinese Academy of Sciences","award":["62175230"],"award-info":[{"award-number":["62175230"]}]},{"name":"Key Program of the Chinese Academy of Sciences","award":["62175232"],"award-info":[{"award-number":["62175232"]}]},{"name":"Key Program of the Chinese Academy of Sciences","award":["YSBR-065"],"award-info":[{"award-number":["YSBR-065"]}]},{"name":"Key Program of the Chinese Academy of Sciences","award":["YJKYYQ20200001"],"award-info":[{"award-number":["YJKYYQ20200001"]}]},{"name":"Key Program of the Chinese Academy of Sciences","award":["2022YFB3607800"],"award-info":[{"award-number":["2022YFB3607800"]}]},{"name":"Key Program of the Chinese Academy of Sciences","award":["2022YFB3605800"],"award-info":[{"award-number":["2022YFB3605800"]}]},{"name":"Key Program of the Chinese Academy of Sciences","award":["2022YFB4601501"],"award-info":[{"award-number":["2022YFB4601501"]}]},{"name":"Key Program of the Chinese Academy of 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In this study, we developed a mechanical 3D lidar system and meticulously calibrated the point cloud transformation to monitor specific areas precisely. Based on this foundation, we have devised a novel set of algorithms for obstacle detection within point clouds. These algorithms encompass three key steps: (a) the segmentation of ground point clouds and extraction of track point clouds using our RS-Lo-RANSAC (region select Lo-RANSAC) algorithm; (b) the registration of the BP (background point cloud) and FP (foreground point cloud) via an improved Robust ICP algorithm; and (c) obstacle recognition based on the VFOR (voxel-based feature obstacle recognition) algorithm from the fused point clouds. This set of algorithms has demonstrated robustness and operational efficiency in our experiments on a dataset obtained from an experimental field. Notably, it enables monitoring obstacles with dimensions of 15 cm \u00d7 15 cm \u00d7 15 cm. Overall, our study showcases the immense potential of lidar technology in railway obstacle monitoring, presenting a promising solution to enhance safety in this field.<\/jats:p>","DOI":"10.3390\/rs16101761","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T06:44:31Z","timestamp":1715841871000},"page":"1761","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Development of a High-Precision Lidar System and Improvement of Key Steps for Railway Obstacle Detection Algorithm"],"prefix":"10.3390","volume":"16","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"}]},{"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"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1109\/TITS.2020.2969993","article-title":"Railway Traffic Object Detection Using Differential Feature Fusion Convolution Neural Network","volume":"22","author":"Ye","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pan, H., Li, Y., Wang, H., and Tian, X. (2022). 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