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Research Papers

# Robust Initial Matching of Free-Form Objects Represented by Point Clouds

[+] Author and Article Information
Daoshan OuYang

Husky Injection Molding Systems Ltd., Bolton, ON, L7E 5S5, Canada

Hsi-Yung Feng

Department of Mechanical Engineering, The University of British Columbia, Vancouver, B.C., V6T 1Z4, Canada

Nimun A. Jahangir

Ontario Power Generation, Pickering, ON, L1V 2R5, Canada

Hao Song

Computer Modeling Group Ltd., Calgary, AB, T2L 2K7, Canada

J. Manuf. Sci. Eng 134(2), 021008 (Apr 04, 2012) (9 pages) doi:10.1115/1.4005800 History: Received January 25, 2011; Revised November 22, 2011; Published March 30, 2012; Online April 04, 2012

## Abstract

The problem of best matching two point cloud data sets or, mathematically, identifying the best rigid-body transformation matrix between them, arises in many application areas such as geometric inspection and object recognition. Traditional methods establish the correspondence between the two data sets via the measure of shortest Euclidean distance and rely on an iterative procedure to converge to the solution. The effectiveness of such methods is highly dependent on the initial condition for the numerical iteration. This paper proposes a new robust scheme to automatically generate the needed initial matching condition. The initial matching scheme undertakes the alignment in a global manner and yields a rough match of the data sets. Instead of directly minimizing the distance measure between the data sets, the focus of the initial matching is on the alignment of shape features. This is achieved by evaluating Delaunay pole spheres for the point cloud data sets and analyzing their distributions to map out the intrinsic features of the underlying surface shape. The initial matching result is then fine-tuned by the final matching step via the traditional iterative closest point method. Case studies have been performed to validate the effectiveness of the proposed initial matching scheme.

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## Figures

Figure 1

Delaunay pole spheres and the medial axis transform

Figure 2

Simulated data sets for the evaluation of DPS and Gaussian curvature

Figure 3

A typical established neighborhood for the data point p

Figure 4

NURBS surfaces for generating the simulated point cloud data

Figure 5

Deviations of DPS radii for data with 10% noise with reduced point density

Figure 6

Two actual data sets

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