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TECHNICAL PAPERS

A Dynamic Sensing-and-Modeling Approach to Three-Dimensional Point- and Area-Sensor Integration

[+] Author and Article Information
Yunbao Huang

Mechanical, Materials and Aerospace Engineering, Illinois Institute of Technology, Chicago, IL 60616

Xiaoping Qian

Mechanical, Materials and Aerospace Engineering, Illinois Institute of Technology, Chicago, IL 60616qian@iit.edu

J. Manuf. Sci. Eng 129(3), 623-635 (Nov 03, 2006) (13 pages) doi:10.1115/1.2714585 History: Received June 23, 2006; Revised November 03, 2006

The recent advancement of 3D non-contact laser scanners enables fast measurement of parts by generating a huge amount of coordinate data for a large surface area in a short time. In contrast, traditional tactile probes in the coordinate measurement machines can generate more accurate coordinate data points at a much slower pace. Therefore, the combination of laser scanners and touch probes can potentially lead to more accurate, faster, and denser measurements. In this paper, we develop a dynamic sensing-and-modeling approach for integrating a tactile point sensor and an area laser scanner to improve the measurement speed and quality. A part is first laser scanned to capture its overall shape. It is then probed via a tactile sensor where the probing positions are dynamically determined to reduce the measurement uncertainty based on a novel next-best-point formulation. Technically, we use the Kalman filter to fuse laser-scanned point cloud and tactile points and to incrementally update the surface model based on the dynamically probed points. We solve the next-best-point problem by transforming the B-spline surface’s uncertainty distribution into a higher dimensional uncertainty surface so that the convex hull property of the B-spline surface can be utilized to dramatically reduce the search speed and to guarantee the optimality of the resulting point. Three examples in this paper demonstrate that the dynamic sensing-and-modeling effectively integrates the area laser scanner and the point touch probe and leads to a significant amount of measurement time saving (at least several times faster in all three cases). This dynamic approach’s further benefits include reducing surface uncertainty due to the maximum uncertainty control through the next-best-point sensing and improving surface accuracy in surface reconstruction through the use of Kalman filter to account various sensor noise.

Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Inaccurate data on the feature edges by area scanners

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Figure 2

Incomplete data caused by occlusion in area scanners

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Figure 3

Incomplete data in shiny surface measurement through area scanners

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Figure 4

A dynamic sensing-and-modeling approach for integrating dimensionally heterogeneous sensors

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Figure 5

Reconstructed surface and uncertainty

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Figure 6

Geometric interpretation of the uncertainty of a B-spline curve

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Figure 7

B-spline curve and its convex hull

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Figure 8

B-spline surface and its convex hull

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Figure 9

Uncertainty comparison of two B-spline curve segments

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Figure 10

Uncertainty surface subdivision and extraction

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Figure 11

An example NBP computing process

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Figure 12

A reconstructed surface and its uncertainty

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Figure 13

NBP search process in a B-spline surface

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Figure 14

Sampled incomplete data point-cloud

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Figure 15

Reconstructed surface and its uncertainty before and after dynamic update

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Figure 16

Reconstructed surface and its uncertainty before and after dynamic update

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Figure 17

Reconstructed surface and its uncertainty before and after dynamic update

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Figure 20

Accuracy comparison between reconstructed surfaces from the Kalman filter and least-squares

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Figure 19

Accuracy comparison between reconstructed surfaces from the Kalman filter and least-squares

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Figure 18

Accuracy comparison between reconstructed surfaces from the Kalman filter and least-squares

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