{"title":"三维激光数据高效分割的聚类方法","authors":"Klaas Klasing, D. Wollherr, M. Buss","doi":"10.1109/ROBOT.2008.4543832","DOIUrl":null,"url":null,"abstract":"In this paper we present a novel method for the efficient segmentation of 3D laser range data. The proposed algorithm is based on a radially bounded nearest neighbor strategy and requires only two parameters. It yields deterministic, repeatable results and does not depend on any initialization procedure. The efficiency of the method is verified with synthetic and real 3D data.","PeriodicalId":351230,"journal":{"name":"2008 IEEE International Conference on Robotics and Automation","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"181","resultStr":"{\"title\":\"A clustering method for efficient segmentation of 3D laser data\",\"authors\":\"Klaas Klasing, D. Wollherr, M. Buss\",\"doi\":\"10.1109/ROBOT.2008.4543832\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a novel method for the efficient segmentation of 3D laser range data. The proposed algorithm is based on a radially bounded nearest neighbor strategy and requires only two parameters. It yields deterministic, repeatable results and does not depend on any initialization procedure. The efficiency of the method is verified with synthetic and real 3D data.\",\"PeriodicalId\":351230,\"journal\":{\"name\":\"2008 IEEE International Conference on Robotics and Automation\",\"volume\":\"107 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"181\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Robotics and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROBOT.2008.4543832\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Robotics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROBOT.2008.4543832","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A clustering method for efficient segmentation of 3D laser data
In this paper we present a novel method for the efficient segmentation of 3D laser range data. The proposed algorithm is based on a radially bounded nearest neighbor strategy and requires only two parameters. It yields deterministic, repeatable results and does not depend on any initialization procedure. The efficiency of the method is verified with synthetic and real 3D data.
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