Least Squares Orthogonal Distance Fitting of Curves and Surfaces in SpaceSpringer Science & Business Media, 7 dic 2004 - 127 páginas Due to the continuing progress of sensor technology, the availability of 3-D c- eras is already foreseeable. These cameras are capable of generating a large set of measurement points within a very short time. There is a variety of 3-D camera - plications in the ?elds of robotics, rapid product development and digital factories. In order to not only visualize the point cloud but also to recognize 3-D object m- els from the point cloud and then further process them in CAD systems, ef?cient and stable algorithms for 3-D information processing are required. For the au- matic segmentation and recognition of such geometric primitives as plane, sphere, cylinder, cone and torus in a 3-D point cloud, ef?cient software has recently been developed at the Fraunhofer IPA by Sung Joon Ahn. This book describes in detail the complete set of ‘best-?t’ algorithms for general curves and surfaces in space which are employed in the Fraunhofer software. |
Índice
LeastSquares Orthogonal Distance Fitting | 17 |
Orthogonal Distance Fitting of Implicit Curves and Surfaces | 31 |
Orthogonal Distance Fitting of Parametric Curves and Surfaces | 40 |
6 | 71 |
Conclusions | 85 |
36 | 89 |
References | 92 |
Implicit 2D Ellipse Chap | 101 |
B CMM Software Tools Fulfilling ISO 103606 | 107 |
FHG Matrix of Superellipse and Superellipsoid | 122 |
Otras ediciones - Ver todo
Least Squares Orthogonal Distance Fitting of Curves and Surfaces in Space Sung Joon Ahn Vista previa restringida - 2004 |
Least Squares Orthogonal Distance Fitting of Curves and Surfaces in Space Sung Joon Ahn No hay ninguna vista previa disponible - 2014 |
Least Squares Orthogonal Distance Fitting of Curves and Surfaces in Space Sung Joon Ahn No hay ninguna vista previa disponible - 2004 |
Términos y frases comunes
3-D circle 3-D measurement Algorithms I-III applications closed form computing cost cone convergence coordinate metrology curve/surface curves and surfaces cylinder data points ellipse ellipsoid fitting error distance Euler angles ƏRT f(ag FHG matrix fitting algorithms given point global minimum global minimum distance implementation implicit and parametric implicit features implicit surface initial parameter values inner iteration iteration 4.6 Iteration number Jacobian matrix Least-Squares Levenberg-Marquardt algorithm line/plane linear equation system location parameters measurement points memory space usage minimizing minimum distance condition minimum distance point model feature model fitting Newton method object recognition object reconstruction object surface ODF algorithms ODF problems orthogonal distance fitting outlier elimination Parameter â parameter covariance parameter grouping parameter update parametric features plane point cloud points in Table procedure rotation set of points sparse matrix sphere fitting square sum superellipse superellipsoid terms of form torus x(ag X₁ xyz frame ди дх Эх