The application of computer-aided inspection
integrated with the coordinate measuring machine and laser
scanners to inspect manufactured aircraft parts using robust
registration of two-point datasets is a subject of active
research in computational metrology. This paper presents a
novel approach to automated inspection by matching
shapes based on the modified iterative closest point (ICP)
method to define a criterion for the acceptance or rejection
of a part. This procedure improves upon existing methods
by doing away with the following, viz., the need for
constructing either a tessellated or smooth representation of
the inspected part and requirements for an a priori
knowledge of approximate registration and correspondence
between the points representing the computer-aided design
datasets and the part to be inspected. In addition, this
procedure establishes a better measure for error between the
two matched datasets. The use of localized region-based
triangulation is proposed for tracking the error. The
approach described improves the convergence of the ICP
technique with a dramatic decrease in computational effort.
Experimental results obtained by implementing this proposed
approach using both synthetic and practical data
show that the present method is efficient and robust. This
method thereby validates the algorithm, and the examples
demonstrate its potential to be used in engineering
applications