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1. International Journal of Machine Tools & Manufacture 42 (2002) 889–897
Automated laser scanning system for reverse engineering and
inspection
Seokbae Son, Hyunpung Park, Kwan H. Lee ∗
Department of Mechatronics, Kwangju Institute of Science and Technology (K-JIST), 1 Oryong-dong, Puk-gu, Kwangju, 500-712, South Korea
Received 30 August 2001; received in revised form 5 March 2002; accepted 6 March 2002
Abstract
Recently, laser scanners have been used more often for inspection and reverse engineering in industry, such as for motors,
electronic products, dies and molds. However, due to the lack of efficient scanning software, laser scanners are usually manually
operated. Therefore, the tasks that involve inspection and reverse engineering processes are very expensive. In this research, we
propose an automated measuring system for parts having a freeform surface. In order to automate a measuring process, appropriate
hardware system as well as software modules are required. The hardware system consists of a laser scanning device and setup
fixtures that can provide proper location and orientation for the part to be measured. The software modules generate optimal scan
plans so that the scanning operation can be performed accordingly. In the scan planning step, various scanning parameters are
considered in the generation of optimal scan paths, such as the view angle, depth of field, the length of the stripe, and occlusion.
In the scanning step, the generated scan plans are downloaded to the industrial laser scanner and the point data are captured
automatically. The measured point data sets are registered automatically and the quality of point data is evaluated by checking the
difference between the CAD model and the measured data. To demonstrate an automated measuring system, a motorized rotary
table with two degrees of freedom and a CNC-type laser scanner with four degrees of freedom are used.  2002 Elsevier Science
Ltd. All rights reserved.
Keywords: Automated scanning; Laser scanner; Optimal scan plan; Reverse engineering
1. Introduction
While a conventional engineering process starts with
a design concept, in reverse engineering, a product is
designed by capturing the shape of the real part. The
part that has a freeform surface is usually developed
through the reverse engineering process. Acquiring the
shape data of a physical part is an essential process in
reverse engineering. The quality of the reconstructed sur-
face model depends on the type and accuracy of meas-
ured point data, as well as the type of measuring
device [1,2].
Currently, a CMM (coordinate measuring machine)
and a three-dimensional (3D) laser scanner are widely
used in the fields for shape reverse engineering and qual-
∗
Corresponding author. Tel.: +82-62-970-2386; fax: +82-62-970-
2384.
E-mail address: lee@kyebek.kjist.ac.kr (K.H. Lee).
0890-6955/02/$ - see front matter  2002 Elsevier Science Ltd. All rights reserved.
PII: S0890-6955(02)00030-5
ity inspection. Most CMMs use a trigger-type probe, but
the ones with mechanical analogue scanning probe do
exist. The trigger-type CMM acquires point data by
touching the probe to the part, such that it is appropriate
for measuring primitive features that need small number
of point data. The scanning-type CMMs can capture
more sampling points than the touch trigger-type and
have better accuracy than vision sensors. They can be
used for measuring freeform features [3], however, they
cannot measure a part made of soft materials and have
relatively lower scanning speed compared to laser scan-
ners. Laser scanners, on the other hand, can obtain a
large amount of point data by non-contact method in a
short time. Since the accuracy of the laser scanner is
getting improved, they are widely adopted for many
applications in industry.
In laser scanning of complex 3D parts, it is difficult
to determine the number of necessary scans, the direc-
tions of scans and scan paths since the device has several
optical constraints such as depth of field (DOF), field of

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view (FOV), and self-occlusion [4,5,6,16]. It takes much
time and cost due to trial and errors when the parts are
scanned manually. In order to resolve this problem, an
automated measuring system, in which scan plan gener-
ation and scanning are performed automatically, is
needed [9,10,17]. Moreover, for scanning the reflective
or transparent materials, preprocessing with a proper
spray is required.
In this research, an automated scanning system for
reverse engineering and inspection of a freeform surface
is developed. The system can generate an optimal scan
plan, which includes the number of required scans, the
scan directions, and the scan paths considering various
parameters of the equipment. In order to implement an
automated system, automated part setup is necessary,
and a motorized rotary table is used for this purpose.
Upon measuring, the axes of the rotary table are known,
and the table is automatically rotated to orient the part
exactly. The generated scan paths are then downloaded
to the laser scanner and the scanning operation is perfor-
med. The captured point data is registered automatically
and the quality of the point data is analyzed.
2. Background and research scope
2.1. Basics of laser scanning system
The mechanism of the 3D laser scanner used in this
research is illustrated in Fig. 1. A laser stripe is projected
onto a surface and the reflected beam is detected by CCD
cameras. Through image processing and triangulation
method, three-dimensional coordinates are acquired. The
laser probe is mounted on a three-axis transport mech-
anism and moves along the scan path that consists of a
series of predetermined line segments. It also rotates in
two directions.
When the laser scanner captures an image, the system
automatically finds an optical focus and keeps the stand-
off distance. The length of laser stripe and the stand-off
Fig. 1. Laser scanning mechanism.
distance cannot be changed by an operator. Since a laser
scanner consists of optical sensors and mechanical mov-
ing parts, various constraints must be satisfied when
measuring a certain point on a part (Fig. 2). The goal of
this section is to generate an optimal scan plan that satis-
fies the following major constraints [6]:
1. View angle: the angle between the incident laser
beam and the surface normal of a point being meas-
ured should be less than the maximum view angle g
di앫Niⱖcos(g),
where
di ⫽
L⫺Pi
|L⫺Pi|
.
2. FOV: the measured point should be located within the
length of a laser stripe
(⫺di)앫Biⱖcos冉d
2冊,
where d is the FOV angle
3. DOF: the measured point should be within a specified
range of distance from the laser source
lSTAND⫺
lDOF
2
ⱕ|L⫺Pi|ⱕlSTAND ⫹
lDOF
2
,
where lSTAND and lDOF denotes stand-off distance and
DOF length.
4. Occlusion: the incident beam as well as the reflected
beam must not interfere with the part itself.
5. The laser probe should travel along a path that is colli-
sion-free.
6. If the part is shiny or transparent, preprocessing is
required such as spraying.
Fig. 2. Constraints for laser scanning.

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S. Son et al. / International Journal of Machine Tools & Manufacture 42 (2002) 889–897
2.2. The scope of research
The final goal of this research is to scan a part with
freeform surfaces automatically with minimum human
interaction. The whole process consists of three parts:
scan plan generation; scanning; and registration/analysis.
In the scan plan generation step, the optimal scan plan
is calculated considering various measuring constraints.
The scan plan includes the number of required scans, the
scan directions, and the scan paths [11,12]. In this
research, it is assumed that the CAD model of the part
is available. For calculating the optimal scan, we propose
algorithms using the methods of estimation and modifi-
cation.
In order to scan the part along the generated scan plan,
problems with the part setup and the coordinate trans-
formation of the scan paths should be investigated. For
scanning, the part is setup using a motorized rotary table
with two axes, and the coordinate transformation is done
by a combination of the translation and rotation matrices.
After the calculated scan paths are downloaded to the
scanner, scanning of the part performed.
After scanning is completed, the acquired data from
different scan directions should be combined in one
coordinate system, which is called registration [2,14,15].
Conventional registration method of using features such
as balls (or spheres) is very time consuming. In this
research, registration is done automatically using the
rotation angles of the rotary table. Finally, by calculating
differences between the CAD model and the scanned
data, the quality of the scanned data is verified.
Fig. 3 shows the overall procedure of the proposed
automated measuring system. The details of each part
are explained in later sections.
2.3. Previous research
Although laser scanners have been widely used in
recent years, there exist few researches related to laser
scanning. Some research works related to laser scanning
are briefly summarized next.
Xi and Shu [4] developed a CAD-based path planning
system for a 3D line laser scanner. They tried to maxim-
ize the coverage of the part by finding the best setup for
Fig. 3. Overall procedure.
the field of view of the laser scanner and part orientation.
However, the system focused on the parts with primi-
tive features.
Bernard and Véron [5] developed a new method that
automatically can scan a part using off-line program-
ming of the CMM. To facilitate the data acquisition pro-
cess, a software module called the Paint was used to
find the illuminated region by the laser beam. The sys-
tem, however, needed to be improved in terms of its
scanning efficiency for a complex part.
Zussman et al. [6,7] developed an algorithm that
determined the location of a laser sensor. But their algor-
ithm was only applicable to a 2D profile of a surface
and could not be used to scan an entire surface of an
object. Elber and Zussman [8] developed an algorithm
that can calculate the number of scans and corresponding
optimal scanning directions for a part with a freeform
surface using a surface decomposition method. They
only considered the angle between the surface normal
and the incident beam in determining the scannability of
a point on a surface, and did not consider other con-
straints.
CMMs have been widely used in industrial appli-
cations and some parts of the measuring processes are
similar to that of laser scanners. Among many research
activities, some notable works are described below. Yau
and Menq [9] and Lim and Menq [10] developed an
automated CMM path planning system for the inspection
of a complex part. The system gave collision-free
inspection paths for dies and molds. After the initial scan
path generation, the inspection plans were simulated in
a CATIA robotics module using the ‘Verify’ program.
The inspection of a manufactured part can be planned
in the CAD/CAM environment and executed by the
CMMs in the shop floor automatically.
Spitz et al. [11] introduced the notions of accessibility
and approachability, and described two sets of algor-
ithms for computing accessibility information. One
algorithm performed exact computation on polyhedral
object and was relatively slow, whereas the other used
discrete approximation for increasing the speed. The
discretized algorithm has been tested on real-world parts.

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3. Scan plan generation
3.1. Overall procedure
For a scan plan generation, a complex part must be
segmented into functional surfaces and the scan plan will
be generated for each surface path. Instead of dealing
with the entire surface, the surface is approximated by
a point set sampled from the surface, and scanning of
all sampled points by the minimum number of scans is
the final goal of this algorithm. Using the sampled
points, pairs of points that cannot be scanned together
are obtained and are used for estimating the necessary
number of scans by considering the view angle con-
straint. The intial scan plan, including the scan directions
and scan paths for each scan direction, is generated, then
the DOF and occlusion constraints are tested. If some
points cannot be scanned, an additional direction or a
modified direction is calculated according to the geo-
metric shape. For these new directions, the same pro-
cedures are repeated until a final scan plan is generated.
Fig. 4 shows the overall procedure for the generation of
a scan plan.
3.2. Determination of the initial scan direction
Since the number of sampled points affects the feasi-
bility of a scan plan, a proper number of points should
be sampled. The distance between neighboring points is
used as the criterion in the algorithm. That is, the dis-
tance between neighboring sampled points must be less
than the length of a laser stripe.
In order to scan two points in the same scan, the angle
between the normal vectors of the two points should be
less than two times the view angle. The points that do
not satisfy the constraint are referred to as critical points.
Since the existence of critical points means that the sur-
face cannot be scanned at one time, the required number
of scans is estimated based upon the critical points. After
finding all critical points, those that have a lower angle
Fig. 4. Overall procedure for the generation of a scan plan.
deviation in their normal vectors than the view angle are
grouped. The number of groups represents the required
number of scan directions.
The next step is to calculate the initial scan directions
based on the groups of critical point. First, the maximum
deviation points, C1–1 and C2–1, those that have the
maximum angle deviation in their normal vectors, are
determined among the critical points (Fig. 5). Each
region grows by finding all the sampling points whose
normal vector has an angle deviation smaller than the
user-defined angle with respect to the maximum devi-
ation point. This angle should be chosen considering the
trade-off between the sizes of the regions and the quality
of the scanned data. Finally, the global mean of all the
normal vectors at the points in the group is determined
as an initial scan direction. That is, the initial scan direc-
tion is represented by
Initial scan direction ISDi ⫽ 冘
n
j ⫽ 1
Dj /ni
where Dj is the unit normal vector of each sampling
point and ni is the number of points in region i.
In general, the best scanning data is obtained when
the scan direction and the surface normal vectors are par-
allel to each other. The global mean direction minimizes
the angle difference between the scan direction and sur-
face normals, such that it can be considered as the best
initial scan direction. The concept of determination of
initial scan direciton is illustrated in Fig. 5.
3.3. Scan path generation
The scan path is the collection of line segments that
guide the laser probe during scanning. The scan path of
the laser scanner used in this research consists of a
sequence ID, a starting point, and an ending point. Gen-
erally, high scannability depends on the length of the
scan path, the number of scan paths, and the number of
setup changes. In this algorithm, the length of the total
Fig. 5. Conceptual drawing for generating an initial scan direction.

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S. Son et al. / International Journal of Machine Tools & Manufacture 42 (2002) 889–897
Fig. 6. An example of scan path generation.
scan path and number of scan paths are minimized to
achieve high scannability.
First, the sampling points that can be scanned in the
same scan direction should be grouped respectively (see
Fig. 6). Then the point sets should be projected onto
a 2D plane that is perpendicular to the scan direction.
Secondly, a bounding rectangle with the smallest area is
calculated. In order to compensate for possible errors
caused by approximation, the bounding rectangle should
be offset by lstripe/2. Since the direction of the laser stripe
and scan path are parallel with the y- and x-axes, respect-
ively, the edges of a bounding rectangle are also parallel
with the y- and x-axes. The width of a sub-rectangle
along the y-axis is 1–2 mm shorter than the length of
laser stripe to ensure the data acquisition of the boundary
and to facilitate the surface fitting operation.
The scanned area is the area swept by the laser stripe
along the scan path. Therefore, all sampling points
should be included in the scanned area. Fig. 6 illustrates
this concept where the dotted rectangle represents the
bounding rectangle of the sampling points and the solid
rectangle represents the bounding rectangle of the
scanned area.
3.4. DOF and occlusion checking
After generating the scan path, DOF and occlusion
constraints have to be checked to verify the scan direc-
tion. To check the DOF, the laser beam is simplified as
five lines as shown in Fig. 7(a). If all points where the
beam contacts the surface are located in the DOF region,
the region can be scanned.
Fig. 7. Modifying scan direction (a) due to a DOF problem and (b) due to an occlusion problem.
Occlusion checking uses a similar process compared
to with the algorithm used to check the DOF. In practice,
a point can be scanned as long as the reflected beam
reaches either one of the sensors. However, for more
reliable results, it is assumed that a point is scannable
only when the reflected beam reaches both sensors of
the probe. The triangle connecting the two CCD camera
sensors and the point to be scanned is used for occlusion
checking [Fig. 7(b)]. If either side of the triangle inter-
feres with the surface, an occlusion exists.
For most smooth surfaces, the initial scan plan works
well. However, when the scan plan is not feasible, a
feedback algorithm should be applied to remedy the
problems. The feedback algorithm determines the policy
considering the shape of the region where the problem
occurs.
When a DOF or an occlusion problem occurs, it
should be examined whether the unscannable points can
be scanned using the other scan directions in the scan
plan. If so, the initial scan plan is still valid. If these
points cannot be scanned using any other directions, the
scan direction should be modified, or an additional scan
direction should be created.
When a part is setup as in Fig. 1, the unscannable
points due to the violation of the DOF condition can be
scanned by rotating the scan direction about the x-axis
[Fig. 7(a)]. Whereas, unscannable points due to
occlusion can be scanned by rotating the scan direction
about the y-axis [Fig. 7(b)]. Some points are unscannable
due to the violation of both conditions, which requires
the rotation of both axes. For all unscannable points, if
the directions of rotation conflict with each other, a new
scan direction must be added.
After the modified direction or the new additional
direction is determined, the same procedure that checks
for the scanning constraints is performed, and the feed-
back loop is carried out until proper scanning plan is
generated.

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Table 1
Specifications of the 3D laser scanner (SURVEYOR Model 2030,
Laser Design, Inc.)
Parameters Values
Stand-off distance 149 mm
View angle 80°
Depth of view 40 mm
Field of view (middle) 32 mm
Laser stripe length 15 mm
Sample count 240 points/line
Beam width 0.254 mm
4. Automated laser scanning
4.1. Laser scanning system and part setup
The automated scanning system is implemented using
a laser scanner (Surveyor model 2030, Laser Design
Inc., see Table 1) and a motorized rotary table. Fig. 8
shows the configuration of the scanning system. The
laser scanner is a stripe-type device with three orthog-
onal transportation axes and a rotating probe. In order
to scan a part from any direction, the system must have
six degrees of freedom. Since the laser scanner has four
degrees of freedom, the remaining two degrees of free-
dom are provided by a rotary table.
In order to verify the scan plan and the scan paths, a
test part is designed and prepared by machining an
aluminum workpiece. The test part consists of a complex
freeform surface and five planar surfaces (Fig. 9). The
freeform surface patch of the test part is intentionally
designed so that it cannot be scanned at once run using
a three-axis CNC-type laser scanner.
In order to automate the scanning and the registration
of the captured point data, a part setup process is neces-
sary. The test part should be positioned on the motorized
rotary table. For the localization process, sensing
devices, a laser scanner, a tooling ball, and a dial indi-
cator are required. First, each axis of the rotary table
should be aligned with the axis of the laser scanner using
Fig. 8. The laser scanning system.
Fig. 9. The test part.
the dial indicator mounted on the laser scanner. Sec-
ondly, a specially designed fixture is attached on the
rotary table and is also positioned. The test part will be
located inside the fixture. Fig. 10 shows the alignment
process of the fixture using the dial indicator.
The relationship between each axis of the rotary table
and specifications are already known. Therefore, in order
to localize the test part, at least two centers of rotation
of the rotary table are required. In this study, the rotary
table is rotated about the X-axis and Z-axis. An accurate
tooling ball is scanned several times while rotating the
rotary table about the X- and Z-axes. The center of
rotation for each axis is calculated by fitting the center
points to a circle. The center of the tooling ball is also
calculated by fitting the point data to a sphere. Conse-
quently, every axis information for part localization is
prepared. Then the scan paths generated in Section 3.3
need to be mapped onto the coordinate system of the
laser scanner by using the information of the axes.
4.2. Coordinate transformation of scan paths
As mentioned above, the scan paths have to be
mapped into the coordinate system of the laser scanner
because the surface model has its own coordinate system
and the laser scanner also has own coordinate system
Fig. 10. Setup of a fixture and a rotary table.

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S. Son et al. / International Journal of Machine Tools & Manufacture 42 (2002) 889–897
(Fig. 11). In order to interface different systems, coordi-
nate transformation is required. For the transformation,
the angle of rotation for each axis and the translation
value used for the coordinate transformation are calcu-
lated from the scan direction determined in Section 3.2
and the measured information in Section 4.1.
The coordinate transformation matrix is as follows
[18]:
[M] ⫽ [T]Rotary table
Model [R]q
X-axis[R]f
Y-axis
where the matrix [T]Rotary table
Model translates the scan paths
from the model axis to the axis of the rotary table and
the matrix [R]q
X ⫺ axis rotates the scan paths by q along
the x-axis.
Therefore, the final scan paths can be calculated using
the matrix [M], and the matrix form of the transform-
ation is as follows:
[Final scan paths] ⫽ [Scan paths]Model[M]
where the matrix [Scan paths]Model is the generated scan
paths in the scan path generation module. Consequently,
the final scan paths, which are downloaded into the laser
scanning system, are prepared.
4.3. Automated scanning and registration
Most measuring systems have their own scanning
software and file format. It is therefore necessary to
translate the scanning plan into a specific file format that
the scanning system can read. The laser scanner used in
this study is operated by a proprietary software package
called DataSculpt and, therefore, the generated plan is
translated into the SCN binary file format [19].
The scan plan is generated using the scan path gener-
ation module explained in Section 3.3. Fig. 12(a) shows
the surface model of the test part and Fig. 12(b) shows
the generated scan directions and paths graphically. Fig.
12(c) shows the screen dump of the generated scan plan
and this scan plan is translated and downloaded into the
scanning system.
After downloading the translated scan plan into the
laser scanning system, an automated scanning operation
of the test part is performed. The upper surface of the
test part is sprayed with the white powder to prevent
unexpected reflection of the laser beam. The laser scan-
Fig. 11. Coordinate transformation.
ner uses a 15 mm-wide semiconductor laser probe and
it can capture 240 points for each scan. The test part was
scanned in two directions and the total scanning time
took about 10 min, with 0.5 mm steps.
After scanning the whole surface, the scanned point
data must be registered under one coordinate frame to
reconstruct the whole surface model. This process can
be performed automatically because the axis information
is already known. As such, an automated registration
process can save much time by preventing it from tedi-
ous data processing work. However, because the size of
acquired point data is very large, it is necessary to reduce
it before further surface modeling and NC code gener-
ation (Fig. 13).
4.4. Analysis of scanning data
The scanned point data usually includes errors from
the moving system, the sensor and the positioning sys-
tem. Errors are usually visualized using the changes of
curvatures and normal vectors of the fitted surface [13].
In order to verify the accuracy of the laser scanning
system and the registration process, the deviation
between the nominal CAD model and the measured
point data at each point is directly calculated. The devi-
ation map gives a clear measuring error range for the
part.
In order to analyze the quality of the measured point
data, a data localization process has to be performed.
Data localization places a measured point data in the
same reference as the CAD model. The concept of data
localization is shown in Fig. 14. Typically the measured
data are moved to a target geometry or CAD model via
different types of data fitting methods such as the 3-2-
1 fitting, the least square fitting, or the min–max fitting
[14,15]. The selection of the data localization method
depends on the shape of part and the required tolerance.
In this system, the data localization process is perfor-
med automatically using the coordinate transformation
method explained in Section 4.2 because the axis
relationship between the measured data and the CAD
model is already calculated. Fig. 15 shows the result of
data localization between the measured point data and
the upper surface of the test part. In the figure, the curve-
net model is designed in the CAD system and the point
data is scanned by the laser scanner.
The measuring error is estimated using the surface-
cloud difference [20]. The results of error estimation are
shown in Table 2. There were few points which have a
big deviation from the surface model. Therefore, it can
be expected that the average deviation and standard devi-
ation will be relatively very small. Those points, which
have big deviations, are easily removed by using a col-
ored deviation map and it improves the quality of the
measured data.
Final error is the addition of all the error sources from

8. 896 S. Son et al. / International Journal of Machine Tools & Manufacture 42 (2002) 889–897
Fig. 12. Generated scan plan: (a) the surface model with normals; (b) scan directions; and paths (c) screen dump of scan path.
Fig. 13. The registered scan data.
Fig. 14. Data localization.
Fig. 15. Result of data localization.

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S. Son et al. / International Journal of Machine Tools & Manufacture 42 (2002) 889–897
Table 2
The error between the CAD model and captured data (unit: mm)
Maximum Average Standard
deviation deviation deviation
Positive value 0.19817 0.05278 0.04087
Negative value –0.18627 –0.05178 0.03894
the rotary table, the laser scanner, the fixture, the spray
material, the part setup method, etc. In order to increase
the accuracy of the measured data, selection of the scan-
ning device, part setup, and scan plan generation are
all important.
5. Conclusion
In this paper, an automated laser scanning system is
proposed. The system can automatically generate a scan
plan by investigating a complex freeform part whose
CAD model is given. The scan plan includes the number
of scans, the scan directions and the scan paths. Also,
the angle of rotation and translation value required for
the coordinate transformation is extracted from the scan
direction information. With these values, the part can
automatically be positioned and scanned precisely in a
short time using a motorized rotary table. The automated
part positioning system can save much time and improve
the quality of captured data. Also, the registration pro-
cess is simplified, thereby, redundant data processing is
drastically reduced and errors caused by human operator
can be minimized.
The developed system is more applicable to inspection
than to genuine reverse engineering because we assumed
that the CAD model of the part is given. So, in future
work, we will expand our system to unknown parts.
Acknowledgement
This work was supported by Grant No. 2000-1-30400-
006-3 from the Basic Research Program of the Korea
Science and Engineering Foundation.
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