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ASTM Committee E57 on 3D Imaging Systems
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 September 2006 Feature
John Palmateer earned bachelor’s degrees in physics and mathematics, a master’s degree in electrical engineering; and a juris doctor in law. His 28-year long career at Boeing has focused on development and implementation of metrology systems, including: computer aided theodolites, tracking interferometers, optical layup templates, laser radar, and structured light systems, as well as related topics such as calibration and certification of these devices.

ASTM Committee E57 on 3D Imaging Systems

A User’s Perspective

The airframe industry was one of the first to embrace large-scale 3D metrology. At its inception, sparse XYZ data from theodolite measurement systems and tracking interferometers were primarily used for point-to-point measurements. This sparse data was also used in the initial forays into reverse engineering. Improvements to computer aided design systems created possibilities of a CAD-CAV-CAM (design, verification, manufacturing, respectively) loop for reducing cycle times and improving products. With the added sophistication of metrology graphical user interfaces came the ability to use CAD surfaces for comparison of the data collected as well as moving into part coordinates. Data collection technology also progressed. Development of laser radar and improvements to digital imaging chips has created an explosion of 3D imaging with the capability of rapidly scanning objects, collecting thousands and millions of data points.

Despite technical improvement, there has been little progress on a unified way of quantifying and comparing the capabilities of these systems. Manufacturers have different ways of expressing system performance, most often position, range, azimuth and elevation resolution or uncertainty with no information on lateral resolution. The systems are technically diverse, and a fair method of expressing and comparing measurement capability is needed. Conventions for measuring and expressing the fidelity of the data, that is, how well the data collected can reconstruct the measured object, have not been established. The impact of device physics such as laser spot size and the convolution of a curved object onto a flat sensor array also need to be considered as part of the investigation into data fidelity.

ASTM International’s Committee E57 on 3D Imaging Systems brings together a community of manufacturers, users, and technical societies in the interest of answering some of these questions and with a goal of developing standards for comparing and calibrating 3D imaging systems. The systems currently under consideration for these standards are non-contact and collect large amounts of data such as laser radar and structured light measurement systems.

Data Fidelity

Data fidelity (not yet defined by Committee E57) is essentially how well collected data can reconstruct the measured object. Fidelity should include components of both lateral resolution and depth resolution for a single measurement or group of measurements. The familiar Nyquist criteria states that a signal can be reconstructed at up to one-half of the sampling frequency. So, in theory a sampled signal can be reconstructed, but in practice bandwidth, sample jitter, noise in the measured signal, etc., limit the ability to reconstruct the original signal. These phenomena have been well studied in communication theory.

Not so for 3D imaging systems. Clearly, the idea that a measured surface can be reconstructed up to one-half of the sampling frequency holds, however, realities of measuring an object with a given 3D imaging system and its data collection characteristics have not been well documented. For example, the density of the data should dictate the fidelity, however, device physics, such as spot size, may limit fidelity of the data. A laser radar measurement system may have a lateral pointing resolution of, say, 1 millimetre, but if this is combined with a spot size of 8 mm, the ability to distinguish objects smaller than 8 mm may be impeded. Additionally, the type of laser radar will impact the fidelity. Does the laser radar average range over the size of the spot? Or does it return the range from a dominant location within the spot whose phase is stable?

Development of a measure of fidelity, similar to the modulation transfer function used in photography, may help users determine measurement limitations and just how well an object can be reconstructed. And it will provide uniform information for comparing 3D imaging systems for a specific application.

Object Reconstruction

Accurate reconstruction from measured data (often called reverse engineering) relies on the fidelity of the measured data. Higher fidelity is required for the reconstruction of smaller details. In most cases, measurement noise is reduced by smoothing and resampling. The resampling can also be used to structure the data (e.g., creating data planes through an object) so that reconstruction software operates more easily. Knowledge of measurement fidelity (e.g., the lateral and depth resolutions) will aid in the filtering and resampling process by providing criteria for distinguishing real and false artifacts.

Traditionally, reverse engineering has relied on a priori knowledge of the object being reconstructed. A trivial example is the measurement of a square corner. The 3D image will collect points in the region of the corner, but probably not measure exactly into the apex of the corner. Even so, without more information, the point cloud cannot be used for reconstructing the square corner because the lack of data density combined with noise in the measurements will round the corner. So in the example of the square corner, the drawing or observation provides the added information to square the corner.

In an automated process, human intervention is not desirable. Comparing measurements to CAD for quality purposes may be limited by the ability to distinguish fine structure or edges. Knowledge of data fidelity will assist the process by providing information on the type of measurement system to use in the data collection process (i.e., selecting a 3D imaging system that can resolve features of interest), or set limits on the items that can be compared to CAD. Another automation possibility is having an intermediate analysis drive the density. So, if during reconstruction of an object the curvature is close to the theoretical limit set by the data density, the measurement system might be able to add measurements increasing the data density and providing a better reconstruction.

Comparison and Calibration

3D imaging systems are very diverse. Comparing and selecting a 3D imaging system for a particular application can be daunting because manufacturers have no common language for expressing the measurement characteristics. Point-to-point calibration seems straightforward, however, the correlation of measurement to a “point” may involve collecting a single measurement or processing a group of measurements to obtain a point (e.g., computing the center of a sphere from points on its surface).

When comparing measurement systems, should differences in data collection methods be taken into account, and if so, how? When defining fidelity, what does it really consist of? Lateral resolution? Depth resolution? Should the result be expressed in the frequency domain or spatial domain? What are the measurement qualifiers? Spot size? Range? Device physics that affect measurements? Are there object characteristics that impact the measurements, such as translucence and curvature, and are there ways for users to quantify these characteristics? There are many practical metrology questions to be addressed in the process of developing 3D imaging standards.

Conclusion

The ASTM Committee E57 on 3D Imaging Systems will aid the measurement system-using community by providing an opportunity to investigate some of the issues regarding device physics and establishing fair standards for comparing and calibrating 3D imaging systems. The manufacturers, users, and technical societies on the committee should be able to work through conflicts due to differences in their diverse technologies, intended markets, and measurement needs to establish consensus standards. //

 
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