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Proceedings Paper

Machine-vision-based alignment: space to factory to garage
Author(s): Donald J. Christian; Hoshang Shroff
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Paper Abstract

Machine vision is an enabling technology for many applications but 'alignment' is arguably the most useful application class. Alignment is the task of 'finding the position of a landmark or work piece in the electronic image' so that it can be tracked, moved, followed, or otherwise adjusted. Many early alignment applications were in aerospace and defense. The visual 'landmark' they used was a star, a constellation or a laser-designated target. These applications made possible highly stable satellite platforms, accurate antenna aiming, and accurate military ordinance that are simply not possible with any other technology. These 'aiming' applications were extensions of traditional gunsighting techniques and nautical navigation. In factory automation, vision-based alignment continues to play a key role in the semiconductor and electronics manufacturing revolution. Robotic machinery requires precision guidance to mate work pieces (dice and printed wiring boards) with process machinery (bonders, saws, and robots). Machine vision technology arrived just in time to make this possible, and new developments continue to improve precision and productivity in this area. New alignment applications are emerging in unexpected areas, such as the automotive service garage. This paper describes a new automotive service application for vehicle wheel alignment. Two machine vision cameras measure the position and attitude of four wheel-mounted targets as the vehicle rolls and is steered. Six axes of rotation are used to define locations and orientations of the axles in three dimensional space. Their values are visibly inferred and measured, and their geometric relationships computed. The measurements are compared against the vehicles' ideal design tolerances for adjustment and repair purposes.

Paper Details

Date Published: 18 September 1997
PDF: 8 pages
Proc. SPIE 3205, Machine Vision Applications, Architectures, and Systems Integration VI, (18 September 1997); doi: 10.1117/12.285581
Show Author Affiliations
Donald J. Christian, Snap-On Diagnostics (United States)
Hoshang Shroff, Snap-On Diagnostics (United States)


Published in SPIE Proceedings Vol. 3205:
Machine Vision Applications, Architectures, and Systems Integration VI
Susan Snell Solomon; Bruce G. Batchelor; John W. V. Miller, Editor(s)

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