inbul 2004
ages taken
ree images
nd Mayer,
rom multi-
Animation
vertical fa-
EEE Com-
ind Pattern
1 approach
exture. In:
—338.
ple consen-
is to image
ions of the
Xf 3d build-
PRS Com-
’hotogram-
38—143.
> 1), 2003.
pen-source
san, Korea.
2003. De-
ion. IEEE
B., 1999.
nd mainte-
| Teller, S.,
» from real-
op on Tex-
ACCURACY ENHANCEMENT OF VISION METROLOGY THROUGH AUTOMATIC
TARGET PLANE DETERMINATION
J.O. Otepka ^', C.S. Fraser ^
“ Institute of Photogrammetry & Remote Sensing, Technical University of Vienna, Gusshausstrasse 27-29/E122,
1040 Vienna, Austria — jo@ipf.tuwien.ac.at
^ Department of Geomatics, University of Melbourne, Victoria 3010, Australia — c.fraser@unimelb.edu.au
Commission V, WG V/1
Keywords: Vision Metrology, Targets, Surface, Automation, Algorithms, Precision, Close Range Photogrammetry
ABSTRACT:
In digital close-range photogrammetry, commonly referred to as vision metrology, circular targets are often used for high precision
applications. The most common target type used consists of retro-reflective material, which provides a high contrast image with flash
photography. The measurement of image coordinates of signalised targets continues to be a factor limiting the achievable accuracy
of high-precision vision metrology systems. Mathematical algorithms are used to determine the centres of imaged targets in 2D
space. These 2D centroids are then used in a triangulation process to calculate the target position in 3D space. This computational
process assumes that the targets represent perfect ‘points’ in space. However, in practice target-thickness and target-diameter can
adversely effect this assumption, leading to the introduction of systematic errors and to incorrect calculation of 3D position. The
paper presents the development of a target plane determination process, which will serve to automatically correct for these errors.
This will also lead to high accuracies within the bundle adjustment via an improved mathematical model.
1. INTRODUCTION
Nowadays, vision metrology (VM) is regularly used in large-
scale industrial manufacturing and precision engineering. The
flexibility of the vision-based concept, combined with new
developments such as high-resolution digital cameras and new
computational models, has made digital close-range
photogrammetry a highly-automated, high-precision three-
dimensional (3D) coordinate measurement technology.
VM strategies employ triangulation to determine 3D object
point coordinates. To achieve accuracy to a few parts per
million, special targets are used to mark points of interest.
Various investigations have shown that circular targets deliver
the most satisfying results regarding accuracy and automated
centroid recognition and mensuration. For such targets, retro-
reflective material is widely used because on-axis illumination
of these targets returns many times more light than a normal
textured surface. This results in high contrast images, which are
a key requirement for high precision in digital close-range
photogrammetry.
The perspective properties of circular targets are such that a
circle viewed from directions other than normal to the target
surface will appear as an ellipse or in general as a conic section,
as indicated in Figure |. Parabolic and hyperbolic curves appear
only if the circle touches or intersects the “vanishing plane”
(the plane parallel to the image plane, which includes the
projection centre). Because of the typically small size of targets
employed and the limited field of view of the measuring
devices, it is unlikely that these circular targets will appear as
parabolic or hyperbolic curves. Therefore, only elliptical
images are considered in the following.
* Corresponding author
image plane
cricle
vanishing plane
Figure 1. Perspective view of circle.
VM systems use centroiding algorithms to compute the centres
of each ellipse, these centroids becoming the actual
observations for the triangulation process. However, it is well
known that the centre of a circle does not project onto the
centre of the ellipse, which thus introduces a systematic error in
any triangulation process. There are two reasons why this error
is universally ignored in today’s VM systems. First, the effect is
small, especially in case of small targets (Dold, 1996; Ahn et.
al, 1997). Second, to date there has been no practical method to
automatically determine the target plane, which is a basic
requirement for the correction of this error.
This paper presents an automatic target plane determination
process which is applicable to any VM network using circular
targets. As will be pointed out, ultra-precise surveys should
benefit from the developed process because the systematic
eccentricity error can be corrected, which will result in higher
obtainable accuracy. However, even medium to high-precision
applications can employ the target plane information to
automatically compensate for target thickness in the case of
