{"title":"自主地面车辆三维点云分割","authors":"Danilo Habermann, A. Hata, D. Wolf, F. Osório","doi":"10.1109/SBESC.2013.43","DOIUrl":null,"url":null,"abstract":"Point clouds segmentation is an essential step to improve the performance of obstacle detection and classification in areas of autonomous ground vehicles and mobile robotics. This paper presents a study and comparison of the performance of segmentation methods using point clouds coming from a 3D laser sensor, more specifically obtained from a Velodyne HDL32.","PeriodicalId":359419,"journal":{"name":"2013 III Brazilian Symposium on Computing Systems Engineering","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"3D Point Clouds Segmentation for Autonomous Ground Vehicle\",\"authors\":\"Danilo Habermann, A. Hata, D. Wolf, F. Osório\",\"doi\":\"10.1109/SBESC.2013.43\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Point clouds segmentation is an essential step to improve the performance of obstacle detection and classification in areas of autonomous ground vehicles and mobile robotics. This paper presents a study and comparison of the performance of segmentation methods using point clouds coming from a 3D laser sensor, more specifically obtained from a Velodyne HDL32.\",\"PeriodicalId\":359419,\"journal\":{\"name\":\"2013 III Brazilian Symposium on Computing Systems Engineering\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 III Brazilian Symposium on Computing Systems Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SBESC.2013.43\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 III Brazilian Symposium on Computing Systems Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBESC.2013.43","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
3D Point Clouds Segmentation for Autonomous Ground Vehicle
Point clouds segmentation is an essential step to improve the performance of obstacle detection and classification in areas of autonomous ground vehicles and mobile robotics. This paper presents a study and comparison of the performance of segmentation methods using point clouds coming from a 3D laser sensor, more specifically obtained from a Velodyne HDL32.