HIGH ACCURACY DIMENSIONAL MEASUREMENT USING NON-
TARGETED OBJECT FEATURES
Armin Gruen and Dirk Stallmann
Institute of Geodesy and Photogrammetry
ETH-Hoenggerberg, CH-8093 Zurich, Switzerland
Tel.: +41-1-3773038, Fax: +41-1-3720438, e-mail: Armin@p.igp.ethz.ch
Commission V
ABSTRACT
In industrial measurement applications a great variety of tasks require the measurement of non-targeted
“natural”) objects. This paper describes a novel approach to the Multi-Photo Geometrically Constrained
(MPGC) matching algorithm which allows for high accuracy dimensional positioning of these features.
Depending on the quality of the feature definition a relative accuracy of 1:25000 is attainable. The major
algorithmic aspects will be addressed. An example from the measurement of a test object will demonstrate the
performance and potential of this new approach.
KEY WORDS: Industrial inspection, image matching, edge measurement, high accuracy
1. INTRODUCTION
It has been shown that under ideal conditions image
edges can be measured with very high accuracy (e.g.
0.006 pixels in Raynor, Seitz, 1990). In many practical
applications, in particular in dimensional industrial
inspection tasks, the highly accurate measurement of
natural, non-targeted object features (e.g. edges) is
required. With the current off-the-shelf sensor-, camera-,
signal transfer- and A/D conversion technology we
anticipate a relative accuracy of 1:25000 in object space
to be attainable. This paper describes a new automatic
measurement algorithm which can potentially deliver
such accuracies. The algorithm is applied in a 3-D-vision
system for precise measurements of industrial parts. The
algorithm is a modification and extension of the MPGC
algorithm. It finds the edge, matches respective patches
in multiple images (theoretically unlimited in number)
and determines 3-D object coordinates simultaneously.
The paper gives a short description of the basic
algorithm, its implementation and the results of a
practical accuracy test. A complete algorithmic treatment
can be found in Gruen, Stallmann, 1991.
The main features of this algorithm are:
e The free selection of image edge templates (synthetic
or real, varying edge spread functions, different edge
types, straight and curved edges).
e Use of collinearity constraints for the imaging rays, to
rcach a high precision and reliability and substantial
reduction of the solution space.
e Additional image space constraints to force
translations of the edge template to be perpendicular to
the edge in the image.
e An unlimited number of sensor frames to be included
in the matching procedure.
e The simultaneous estimation of object space
coordinates with measures for quality control of the
algorithm.
e The application of the algorithm for an image/object
tracking procedure.
A typical part to be measured is an aeroplane engine
nozzle as shown in Figure 1 with 25 cm diameter and 5
cm height. This type of object has a complex surface
shape with pulse and step edges, corners, surface paint
patches, different surface orientations and mixed surface
SSSR ES
Figure 1 Aeroplane engine nozzle. Image acquired
with a Videk Megaplus camera (1024 x
1024 pixels), image brightness and contrast
enhanced with a Wallis filter.
