Download Least Squares Orthogonal Distance Fitting of Curves and by Sung Joon Ahn PDF

By Sung Joon Ahn

Due to the ongoing development of sensor know-how, the supply of 3-D c- eras is already foreseeable. those cameras are in a position to producing a wide set of size issues inside of a really couple of minutes. there's a number of three-D digicam - plications within the ?elds of robotics, fast product improvement and electronic factories. that allows you to not just visualize the purpose cloud but in addition to acknowledge 3-D item m- els from the purpose cloud after which additional method them in CAD platforms, ef?cient and strong algorithms for 3D details processing are required. For the au- matic segmentation and popularity of such geometric primitives as aircraft, sphere, cylinder, cone and torus in a 3-D element cloud, ef?cient software program has lately been constructed on the Fraunhofer IPA via Sung Joon Ahn. This booklet describes intimately the whole set of ‘best-?t’ algorithms for normal curves and surfaces in area that are hired within the Fraunhofer software.

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Additional resources for Least Squares Orthogonal Distance Fitting of Curves and Surfaces in Space

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In the case of motion analysis - which is a substantial task carried out in robotics, ergonomics, sport science, and entertainment industry techniques - the motion parameters of a moving object are determined by fitting curves to the sampled points of the object trajectory. Model fitting to the measurement points of real objects is a central task in coordinate metrology, reverse engineering, and CAD/CAM, where the workpieces require a mathematical description with their model parameters (parametric model recovery, Fig.

1) is required which usually has only a few form parameters. 3) are the same for all implicit features. 1 describes two generally applicable algorithms for finding the minimum distance point on an implicit feature from a given point. 32) for implicit features. Various fitting examples are given in Sect. 3. 36 3. 1) is the inner iteration which finds the minimum distance points on a general model feature from each given point For a given point in xyz frame (Fig. 1) defined in xyz frame. Then the minimum distance point in XYZ frame from the given point can be obtained through the backward transformation of into XYZ frame as below For some implicit features such as a circle, sphere, cylinder, cone and torus, the minimum distance point can be determined in closed form at low computing cost.

15). 1 Problem Definition The ultimate goal of the orthogonal distance fitting of a model feature to a set of given points in space is the determination of the model parameters which minimize the square sum of the minimum distances between the given points and the model feature. e. not only the square sum but also every single distance between the given points and the model feature should be minimized. 2 Generalized Orthogonal Distance Fitting 23 where X : coordinate column vector of the coordinate column vector of the on the model feature, d : distance column vector, given points corresponding points with weighting matrix or error covariance matrix (positive definite) P : nonsingular symmetric matrix [28].

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