2009 IEEE International Conference on Robotics and Automation
Kobe International Conference Center
Kobe, Japan, May 12-17, 2009

Laser-based Geometric Modeling using Cooperative
Multiple Mobile Robots
Ryo Kurazume, Yusuke Noda, Yukihiro Tobata, Kai Lingemann, Yumi Iwashita and Tsutomu Hasegawa
Abstract— In order to construct three-dimensional shapemodels of large-scale architectural structures using a laser
range ﬁnder, a number of range images are taken from various
viewpoints. These images are aligned using post-processing
procedures such as the ICP algorithm. However, in general,
before applying the ICP algorithm, these range images mustbe aligned roughly by a human operator in order to converge
to precise positions. The present paper proposes a new modelingsystem using a group of multiple robots and an on-board
laser range ﬁnder. Each measurement position is identiﬁedby a highly precise positioning technique called Cooperative
Positioning System (CPS), which utilizes the characteristicsof the multiple-robot system. Thus, the proposed system canconstruct 3D shapes of large-scale architectural structures
without any post-processing procedure or manual registration.
ICP is applied optionally for a subsequent reﬁnement of
the model. Measurement experiments in unknown and large
indoor/outdoor environments are carried out successfully using
the newly developed measurement system consisting of threemobile robots named CPS-V. Generating a model of Dazaifu
Tenmangu, a famous cultural heritage, for its digital archive
completes the paper.

I. INTRODUCTION
For constructing 3D models of large-scale architectural
structures using laser range ﬁnders, a number of range
images are taken from various viewpoints. These images arealigned using post-processing procedures, such as the ICP
algorithm [1], [2]. This is necessary, because a laser range
ﬁnder cannot cover the entire area at one time due to thelimitation of measurable distance and occlusion problems.
After post-processing procedures, the range images describedin the sensor coordinate system are transformed to the global
coordinate system, and the entire shape of the architecture is
obtained. However, in general, when sufﬁciently exact scanpose estimates are not given, a human operator registers therange images to correct positions roughly before applying
the ICP method in order to ensure that the images converge
to proper poses. This procedure is quite laborious and timeconsuming and is considered to be a signiﬁcant obstacle fordeveloping an automatic 3D laser measurement system. In
addition, all of the images must contain sufﬁcient feature
R. Kurazume, Y. Noda, Y. Tobata, Y. Iwashita, and T. Hasegawa
are with Graduate Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, Japankurazume@is.kyushu-u.ac.jp
Y. Noda is with Graduate School of Information Science and ElectricalEngineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 8190395, Japan
Y. Tobata is with LinX Corporation, 1-13-11, Eda-nishi, Aoba-ku, Yokohama, 225-0014, Japan
Kai Lingemann is with University of Osnabrück, Albrechtstr. 28, 31/506,
D-49069, Osnabrück, Germany

978-1-4244-2789-5/09/$25.00 ©2009 IEEE

shapes and must sufﬁciently overlap each other, which requires dense scanning from a number of positions, in orderto register range images precisely by the ICP algorithm.
Another approach that requires no post-processing procedures such as the ICP algorithm can also be considered.
It involves the precise identiﬁcation of the position of therange sensor at each measurement. Since this method canobtain the transformation matrix from the sensor speciﬁccoordinate system to the global coordinate system, local
range images are converted directly into the global coordinate
system with a simple transformation calculation. As an
example of this approach, several systems that utilize GPS[3], [4] to determine the position of the range sensor havebeen proposed. However, special instruments and techniques,
such as a Real-time Kinematic (RTK) system or the Virtual
Reference Station (VRS) method, are required in order toachieve highly precise position identiﬁcation by current GPS.
Moreover, GPS cannot be used unless a sufﬁcient number ofsatellites can be accessed which leads to problems in, forexample, narrow structures, forests, or indoor environments.
The present paper proposes a new 3D measurement system for large-scale architectural structures using a groupof mobile robots and an on-board laser range ﬁnder. The
proposed system uses the Cooperative Positioning System(CPS) [5], [6] for multiple robots, which has been proposed
as a highly precise position identiﬁcation technique formobile robots, and an automatic measurement system forlarge-scale architectural structures is realized by combining
highly precise position identiﬁcation by CPS and an onboard laser range ﬁnder. This system can construct a 3Dmodel of a large-scale architectural structure without any
post-processing procedure such as the ICP algorithm orregistration by a human operator. In addition, it is possible toregister range images even if the number of measurements is
few and only sparse range images, e.g., range images containing insufﬁcient feature shapes or overlappping regions,are available. It is also possible to construct 3D model inenvironments where GPS is not available, such as inside
structures or in an indoor environment.
The proposed system is related to the simultaneous localization and mapping (SLAM) method [7]–[14], which is
attracting a great deal of attention in the robotics community.
As shown in Section III.B, the 3D model obtained by CPS
is reﬁned by applying ICP. Obviously, the reﬁned measurement position from ICP can also be fed back to the CPS
positioning system. This closed loop control will increase
the accuracy of both the 3D model and robot position.
This paper introduces measurement experiments in un-

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known and large indoor/outdoor environments using the
newly developed measurement system consisting of threemobile robots named CPS-V. In addition, as an example ofapplications of the proposed system, we introduce the digitalarchive experiments of Dazaifu Tenmangu, which is Japanese
famous cultural heritage and one of the important culturalproperties of Japan.
II. C OOPERATIVE P OSITIONING S YSTEM (CPS)Let us consider the system in which a mobile robot
equipped with an on-board laser range ﬁnder moves arounda measurement target and scans the target from several
different positions. If all of the measurement positions are
identiﬁed with high accuracy, the range data acquired at eachposition can be converted to the global coordinate system bya simple coordinate transformation calculation.
Several position identiﬁcation methods have been proposed, and these methods can be classiﬁed into four categories:
1) Integrate sensor output from internal sensors such as
wheel encoders or acceleration sensors.
2) Observe external landmarks by external sensors suchas laser range ﬁnders or cameras.
3) Use the Global Positioning System (GPS).
4) The simultaneous localization and mapping (SLAM)
methodMethod (1) is referred to as odometry, or dead reckoning,
and is a commonly used positioning technique, especially forwheeled vehicles. However, there are drawbacks associatedwith this method. For example, the accuracy of position
identiﬁcation in uneven terrain is quite low due to slippageof the wheels, and 3D positioning including elevation is
impossible. Method (2) has high accuracy if landmarks
placed on the moving path can be measured precisely.
However, landmarks must be placed beforehand along themoving path, and a precise map of these landmarks must
be available. Therefore, method (2) cannot be used in an
unknown environment. Method (3) can be considered as a
special case of method (2) and is becoming very popular,
especially for ﬁeld robots. However, this method also has
several drawbacks. For example, active ﬁelds of robots arelimited to an outdoor environment with an unobstructed
view of satellites, and the accuracy is currently not so high,unless special techniques described in Section I are used.
Method (4) is based on the matching of sequentially observed
local features acquired by external sensors such as laser
range ﬁnders or cameras. When considering the features aslandmarks, this constitutes a general case of method (2).
Though this method is a prospective technique for precise
position identiﬁcation in unknown and unstructured environment, precise and sufﬁcient information between consequtive
observations must be acquired through dense measuremnts.
To overcome these limitations of position identiﬁcation
problems and realize accurate positioning of mobile robots,
Kurazume et al. proposed the Cooperative Positioning System (CPS) [5]. In this system, multiple robots with highly

precise measurement devices of mutual positions are controlled cooperatively, and unsurpassed positioning accuracy,
as compared to conventional positioning techniques, is realized, even in unknown and uneven environments.
The basic principle of CPS is as follows: We divide therobots into group A and group B. Group A remains stationary
and acts as a landmark while group B moves. Group B thenstops and acts as a landmark for group A. This alternating
behavior is repeated until the target position is reached. By
using the concept of ”portable landmarks”, CPS has a far
lower accumulation of positioning error than dead reckoning
and can work in three dimensions, which is not possible byordinary dead reckoning. In addition, CPS can be used in
an unknown environment, since there is no need to place
landmarks beforehand.
An example of CPS is shown in Fig.1. This example is fora robot system consisting of a parent robot with a sensing
device such as a laser range ﬁnder, and two child robots.
The sensing device can measure the relative positions of thechild robots from the parent robot. First, we assume that theinitial position of the parent robot is measured or deﬁned
beforehand.
(1) Child robots 1 and 2 are moved and stopped.
(2) The parent robot measures the distance, azimuth, andelevation angles to child robot 1 and identiﬁes the
position of child robot 1.
(3) The position of child robot 2 is identiﬁed in the samemanner as Step 2.
(4) The parent robot moves and stops. The distances,azimuth, and elevation angles to child robots 1 and 2
are then measured, and the position of the parent robot
is calculated using the triangular surveying technique.
(5) Repeat Steps 1 through 4 until the target position isreached.
Though the principle of CPS is simple, the position
calculation which suppresses the error accumulation is rather
complicated [6]. In CPS, though the accuracy is quite high,the measurement errors are gradually accumulating and thecharacteristics of errors depend on the moving histories of
the robots. To minimize the error accumulation by taking themoving histories into account, we proposed the non-linear
least squared method based on the sequential estimation oferror covariance matrices. The performance of CPS has beentested through many experiments in a variety of environments such as an unpaved outdoor environment with steep
sloops, and conﬁrmed that, compared to odometry, a highly
precise robot tracking is achieved [6].
III. C ONSTRUCTION OF A 3D ENVIRONMENTAL MAP BY
MULTIPLE ROBOTSThis section proposes a new measurement system for precise construction of a 3D environmental map by combining
CPS for multiple robots and a laser range ﬁnder. In thissystem, mobile robots move around a large-scale target andscan the target by an on-board 3D laser range ﬁnder fromseveral viewpoints. Each measurement position is precisely
identiﬁed by CPS using a parent robot and two child robots.

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Parent robot

z
x

y

Yaw

1

1

r1

0

0

2

2

2D scanninglaser range finder,
LMS200ψ1
φ1

Total station,
GTS-825A

Corner cubes
Child robots(2) Robot 0 measures
the position of robot 1.

(1) Robot 1 and 2 move.
1
2 r
2

ψ2

0

0
2

φ2

(3) Robot 0 measures
the position of robot 2.

Fig. 1.

r2
φ2

ψ2

r1
ψ1

2-axisattitude sensor

1
φ1

DC servomotor(36 W)(4) Robot 0 moves andmeasures the positionof robots 1 and 2.

(b) Clild robot

Cooperative Positioning System (CPS)(a) Parent robot

First, we introduce the ﬁfth CPS machine model, called CPSV, which is equipped with a 2D laser range ﬁnder and ascanning mechanism, and show the experimental results forthe construction of indoor and outdoor environmental maps
by CPS-V.

Fig. 3. Robots with 3D laser measurement using the rotation table aroundthe yaw axis
TABLE IS PECIFICATIONS OF THE TOTAL STATION , GTS-825A
GTS-825A (TOPCON Ltd.)Range
1.3 ∼ 2,200 [m]Resolution (distance)
0.2 [mm]
Resolution (angle)
3”
Precision (distance)± 2+2ppm [mm]Precision (angle)±5”

A. Fifth CPS machine model (CPS-V)
Figure 2 shows the ﬁfth CPS machine model, CPS-V. Thissystem consists of a parent robot (P-cle, Parent mobile unit,
Fig.3(a)) and two child robots (HPI Japan, Fig.3(b)). The
parent robot is equipped with an on-board 2D laser range
ﬁnder (LMS 200, Sick), a high-precision two-axis attitude
sensor (MD900T, Applied Geomagnetics), and a total station
for surveying (GTS-825A, TOPCON Ltd.)(Tab. I), which isused to measure the relative positions of the child robots.
Even if the body is tilted on a slope, the body inclination iscompensated by an attitude sensor, and precise positions of
the robots is possible. The 2D laser range ﬁnder can acquire
2-dimensional slit-like range data within the range of 80 m
and 180 degrees, as shown in Tab. II. The parent robot has
a rotating table on the top body, and by rotating the tablearound the vertical axis while scanning using the 2D laser
range ﬁnder, 360◦ 3D range images are acquired from theparent robot in 37.8 seconds.

Parent robot

2-axis attitude sensor

Child robot 2
Child robot 1

Fig. 2.

Parent mobile
unit, P-cle

Battery

Celeron
650 MHz

Fifth CPS machine model, CPS-V

B. Construction experiment of the indoor environmental map
1) Initial modeling by CPS-V: Experiments for constructing 3D maps are carried out using CPS-V in an indoor
environment. In this experiments, the parent robot moved,

stopped, and identiﬁed its position 22 times. The positionsof robots are currently determined by an operator so thatthe number of movements of the child robots becomes as
small as possible. After identiﬁng the position, the parent
robot captured 3D range images at each stationary position
by rotating the on-board 2D laser range ﬁnder around thevertical axis. The obtained range images are transformed
into the global coordinate system by simple coordinatetransformation calculation using the position measured by
CPS. No post-processing procedure is applied so far. The
positioning error of CPS is 0.22 m, compared to the total
moving distance of the parent robot of 93.9 m (0.24% of
the distance traveled). The error is measured using reference
corner cubes around the starting position. Figure 7(a) depicts
the difference in measured positions of the corner indicatedin Fig.4. From this ﬁgure, we can estimate that the modelingerror is about 220 mm. Figures 5 and 6 shows the total viewand some images of the obtained 3D map.
2) Optional reﬁnement via ICP: If the scans do display
enough overlap, an additional step for ﬁnalizing the map isapplied: The obtained 3D model is reﬁned automatically using a version of the Iterative Closest Points (ICP) algorithm.
The implemented method registers range images against aso-called metascan, i.e., the union of the previously acquired
and registered scans, and the 3D model is grobally optimized.
A signiﬁcant speed-up is achieved by organizing thedata – as well as reduced data sets based on an octree
representation of the scans – in a k-D tree structure. These
trees are furthermore cached, exploiting a buffer for leaf
nodes; the buffer is used to start backtracking in subsequent
searches.Therefore, the iterative structure of ICP is exploitedfor reducing computation costs. By using these mechanisms,
the registration of the whole 22 scans of this experiment took

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TABLE IIS PECIFICATION OF THE LASER RANGE FINDER , LMS200
LMS 200 (SICK Corp.)
Range
80 [m]
Field of view
180◦
Resolution (distance)
10 [mm]Resolution (angle)
0.5◦

50 m
9.3 seconds only.
Figure 7(b), showing the registration results after applying
ICP, demonstrates the optimal cooperation of both algorithms: The ﬁrst step, alignment by CPS, yields a consistentand mostly accurate map – without human intervention–, which guarantees that the scans’ estimated poses are
sufﬁciently exact for a safe and stable reﬁnement by ICP.
This way, we were able to reduce the modeling error toless than 8.5 mm. In fact, the combined work of these two
methods provided such accurate results that an execution of
global 3D optimizationdid not noticeably change the output
in our experiment.

Fig. 5.

3D map of the indoor environment

(a1)

(a2)

(b1)

(b2)

10 m

Corridor(33.7, 6.3, 0.1)

Upstairs

Hall

(c1)

(9.1, -3.9, 0.1)

Fig. 6.

(c2)
3D model of the indoor environment

Corner13 points in the outdoor environment and constructed 3Dmodels of the building. The path of the parent robot and theobtained maps are shown in Figs.8, 9, and 10, respectively.
In this experiment, we used a small number of measurements from sparse viewpoints. Neither dense range data nor
sufﬁciently overlapped regions, which are indispensable for
applying the ICP algorithm, were available. Based on thisexperiment, we concluded that it is possible to construct 3D
maps of simple-shaped but large-scale architectural structuresusing the proposed system with only a few scans. The
positioning error in this experiment is 0.63 m, compared tothe total travel distance of the parent robot of 147.7 m, which
is 0.43% of the distance traveled.

Path of parent robotx

Initial position(0, 0, 0)Final position(0, 0, 0.2)y: Scanning positions ofparent robot.
: Scanning positions ofparent robot. Childrobots moved after themeasurement.

Downstairs: Positions of child robot 1.
: Positions of child robot 2.

Fig. 4.

Path of the parent robot

C. Construction experiment to obtain a large-scale outdoor
environmental map
Next, we performed a construction experiment to obtain3D environmental maps of large-scale architectural structures. We measured the outer walls of a building from

IV. A PPLICATION FOR THE DIGITAL ARCHIVE OF
LARGE - SCALE CULTURAL HERITAGESThis section introduces an example of the application of
the proposed system for the digital archive of a large-scale3203

23rd scanning

8.5 mm

1st scanning 220 mm(a) Modeling error by CPS
Fig. 7.

(b) After applying ICP

Comparison of positioning error(36.4, -2.5, -0.03)

Fig. 10.
Benchesand Figs. 12 and 13 show the 3D model of the main shrine
and the garden including three famous bridges over the pond
named “Shinji Ike”. The parent robot moved and measured
the Dazaifu Tenmangu from 76 places in the inside andoutside of the main shrine and the garden.

Side of thebuilding
Initial position(0.0, 0.0, 0.0)

3D model of buildings in the outdoor environment(4.3, -3.0, -0.03)
Final position(-1.4, 0.7, -0.03)

Rocks
x
y

Fig. 8.

: P-cle scanning position [m]Path of the parent robot in the outdoor environment

Japanese cultural heritage.
Dazaifu Tenmangu (Dazaifu Shrine) (Fig.11) in Fukuoka,
Japan, was established in 919 by Michizane Sugawara who is
known as the god of leaning. The main shrine of the Dazaifu
Tenmangu was built in 1591 and is registered as an important
cultural property of Japan. The size of the main shrine andthe yard are about 250 m × 100 m.
In August 2008, we successfully carried out the 3D digitalarchive experiment of the main shrine of Dazaifu Tenmangu
and the vast garden by the robot system proposed in thispaper. Figure 14 shows the total view of Dazaifu Tenmangu

Fig. 11.

Fig. 12.

Dazaifu Tenmangu (Dazaifu Shrine)

3D model of the main shrine of Dazaifu Tenmangu

V. C ONCLUSIONS

Fig. 9.

3D map of buildings in the outdoor environment

A new 3D measurement system was proposed for largescale architectural structures using a precise positioning
system called the Cooperative Positioning System (CPS)
involving multiple robots and an on-board laser range ﬁnder.
The proposed system enables to create a 3D model without
post-processing procedures, and can be applied to sparse3204

Fig. 14.

Total view of the 3D model of Dazaifu Tenmangu

R EFERENCES

Fig. 13.

(a1)

(a2)

(b1)

(b2)

Constructed 3D model of Dazaifu Tenmangu

range measurements. ICP can be applied optionally for a subsequent reﬁnement of the model. Therefore, a fully automatic
3D measurement system for large-scale architectural structures, such as large, culturally important structures, will be
realized based on the proposed system. Measurement experiments in unknown and large indoor/outdoor environments
were successfully carried out using the newly developed
multiple-robot system, called CPS-V. Generating a model ofDazaifu Tenmangu, a famous cultural heritage, for its digitalarchive completed the paper.
In the future, we intend to investigate the developmentof an automatic 3D laser measurement system that willautomatically select optimum measurement points. The positioning accuracy of CPS can also be further improvedby back-projection, that is, the observation of some stablelandmarks before and after the movement and the correctionof each measurement position by back-projecting the errors.
In addition, the combination of CPS and the ICP method
not only for the reﬁnement of the model but also theimprovement of the accurate positioning will be considered.

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