{"title":"通过匹配多个三维轮廓图实现光条系统的自校准","authors":"O. Jokinen","doi":"10.1109/IM.1999.805348","DOIUrl":null,"url":null,"abstract":"A novel method is proposed for refining the calibration of a light striping system including a projective transformation between the image plane of the camera and the plane of the laser sheet, and also the direction of the scanning with respect to the plane of the laser sheet. The refinement is obtained through weighted least squares matching of multiple profile maps acquired from different viewpoints and registered previously using an approximate calibration. Testing with synthetically generated profile maps shows that if the geometry of the object is appropriate and the registration parameters and the intrinsic parameters of the system are known exactly, then a calibration accuracy of 0.003...0.00003% relative to the scene dimensions can be achieved as the average noise level in the maps used for the calibration decreases from 0.3 down to zero pixels. It is also possible to adjust several calibrations at the same time. The registration and calibration parameters can be refined simultaneously, but a close initial estimate and rather complex object geometry are needed for an accuracy of 0.03% when the average noise level is 0.03 pixels. Determining the corresponding points by interpolation on the parametric domains of the maps yields higher accuracy than perpendicular projection to the tangent planes at the closest points in 3D in both registration and calibration tasks. The highest accuracy is achieved when the interpolation errors are as equal as possible within the overlapping areas.","PeriodicalId":110347,"journal":{"name":"Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"53","resultStr":"{\"title\":\"Self-calibration of a light striping system by matching multiple 3-D profile maps\",\"authors\":\"O. Jokinen\",\"doi\":\"10.1109/IM.1999.805348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel method is proposed for refining the calibration of a light striping system including a projective transformation between the image plane of the camera and the plane of the laser sheet, and also the direction of the scanning with respect to the plane of the laser sheet. The refinement is obtained through weighted least squares matching of multiple profile maps acquired from different viewpoints and registered previously using an approximate calibration. Testing with synthetically generated profile maps shows that if the geometry of the object is appropriate and the registration parameters and the intrinsic parameters of the system are known exactly, then a calibration accuracy of 0.003...0.00003% relative to the scene dimensions can be achieved as the average noise level in the maps used for the calibration decreases from 0.3 down to zero pixels. It is also possible to adjust several calibrations at the same time. The registration and calibration parameters can be refined simultaneously, but a close initial estimate and rather complex object geometry are needed for an accuracy of 0.03% when the average noise level is 0.03 pixels. Determining the corresponding points by interpolation on the parametric domains of the maps yields higher accuracy than perpendicular projection to the tangent planes at the closest points in 3D in both registration and calibration tasks. The highest accuracy is achieved when the interpolation errors are as equal as possible within the overlapping areas.\",\"PeriodicalId\":110347,\"journal\":{\"name\":\"Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062)\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"53\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IM.1999.805348\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IM.1999.805348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Self-calibration of a light striping system by matching multiple 3-D profile maps
A novel method is proposed for refining the calibration of a light striping system including a projective transformation between the image plane of the camera and the plane of the laser sheet, and also the direction of the scanning with respect to the plane of the laser sheet. The refinement is obtained through weighted least squares matching of multiple profile maps acquired from different viewpoints and registered previously using an approximate calibration. Testing with synthetically generated profile maps shows that if the geometry of the object is appropriate and the registration parameters and the intrinsic parameters of the system are known exactly, then a calibration accuracy of 0.003...0.00003% relative to the scene dimensions can be achieved as the average noise level in the maps used for the calibration decreases from 0.3 down to zero pixels. It is also possible to adjust several calibrations at the same time. The registration and calibration parameters can be refined simultaneously, but a close initial estimate and rather complex object geometry are needed for an accuracy of 0.03% when the average noise level is 0.03 pixels. Determining the corresponding points by interpolation on the parametric domains of the maps yields higher accuracy than perpendicular projection to the tangent planes at the closest points in 3D in both registration and calibration tasks. The highest accuracy is achieved when the interpolation errors are as equal as possible within the overlapping areas.