{"title":"基于icp的位姿测量几何特征评估新方法:连续体形状约束分析","authors":"D. McTavish, G. Okouneva","doi":"10.1109/IMVIP.2007.3","DOIUrl":null,"url":null,"abstract":"This paper presents a generalization of closest- point constraint analysis called continuum shape constraint analysis (CSCA) that can be used to assess the suitability of whole objects or object features for range data scanning and subsequent pose estimation. \"Directional CSCA\" (D-CSCA) is proposed to specifically address pose estimation accuracy via the ICP (iterated closest-point) family of algorithms. Constraint analysis based on noise amplification index (NAI) is used. In the D-CSCA formulation, the continuum nature of the underlying shape registration renders the resulting gradient matrix and NAI thereof as pure properties of the feature, dependent on viewpoint but independent of the viewing instrument.","PeriodicalId":249544,"journal":{"name":"International Machine Vision and Image Processing Conference (IMVIP 2007)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A New Approach to Geometrical Feature Assessment for ICP-Based Pose Measurement: Continuum Shape Constraint Analysis\",\"authors\":\"D. McTavish, G. Okouneva\",\"doi\":\"10.1109/IMVIP.2007.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a generalization of closest- point constraint analysis called continuum shape constraint analysis (CSCA) that can be used to assess the suitability of whole objects or object features for range data scanning and subsequent pose estimation. \\\"Directional CSCA\\\" (D-CSCA) is proposed to specifically address pose estimation accuracy via the ICP (iterated closest-point) family of algorithms. Constraint analysis based on noise amplification index (NAI) is used. In the D-CSCA formulation, the continuum nature of the underlying shape registration renders the resulting gradient matrix and NAI thereof as pure properties of the feature, dependent on viewpoint but independent of the viewing instrument.\",\"PeriodicalId\":249544,\"journal\":{\"name\":\"International Machine Vision and Image Processing Conference (IMVIP 2007)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Machine Vision and Image Processing Conference (IMVIP 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMVIP.2007.3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Machine Vision and Image Processing Conference (IMVIP 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMVIP.2007.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Approach to Geometrical Feature Assessment for ICP-Based Pose Measurement: Continuum Shape Constraint Analysis
This paper presents a generalization of closest- point constraint analysis called continuum shape constraint analysis (CSCA) that can be used to assess the suitability of whole objects or object features for range data scanning and subsequent pose estimation. "Directional CSCA" (D-CSCA) is proposed to specifically address pose estimation accuracy via the ICP (iterated closest-point) family of algorithms. Constraint analysis based on noise amplification index (NAI) is used. In the D-CSCA formulation, the continuum nature of the underlying shape registration renders the resulting gradient matrix and NAI thereof as pure properties of the feature, dependent on viewpoint but independent of the viewing instrument.