Proceedings of the 2010 IEEE
International Conference on Robotics and Biomimetics
December 14-18, 2010, Tianjin, China

Automatic laser-based geometrical modeling using multiple mobile
robotsYukihiro Tobata, Ryo Kurazume, Yumi Iwashita, and Tsutomu Hasegawa
Abstract— This paper presents an automatic 3-D laser measurement system of an environmental structure using thecooperation of multiple mobile robots. The robots are equippedwith two types of laser sensors and measure the 3D environmental structure while moving around the environment
automatically. To identify the measurement positions by the
robots, Cooperative Positioning System (CPS) is utilized which
is a highly precise positioning technique of co-operative multiple
robots. Since the positions of the robots for 3D laser scanning
are identified precisely, the 3D environmental model can beconstructed without any post-processing procedure such as
the ICP algorithm or manual intervention. Therefore, a fully
automatic 3-D measurement system of the environmental model
can be realized. This paper presents the manual and automaticplanning experiments of measurement procedure using thepartially measured structure of the environment. Planningand measurement experiments by three mobile robots are
successfully carried out.

I. INTRODUCTION
Laser-based 3D modeling is a popular technique for constructing a 3D model of a real object. Digital preservation ofhistorical and cultural heritages [1],[2], reverse engineering,search of survivors in collapsed building for rescue operation
are good examples of the laser-based 3D modeling.
For constructing a 3-D model of environment using alaser range finder, a number of range images are takenfrom different viewpoints around the environment since themeasured field by a scan is limited. Then, these images
are aligned using post-processing procedures, such as the
ICP algorithm[3], [4]. After the post-processing procedures,the range images described in the sensor co-ordinate system
are transformed to the global co-ordinate system, and theentire shape of the architecture is obtained. However, ingeneral, when sufficiently exact scan pose estimates are notavailable, a human operator registers the positions before
applying the ICP method in order to ensure that the images
converge to the proper poses. This procedure is quite timeconsuming and is considered to be a significant obstaclefor developing an automatic 3-D laser measurement system.
Also, all of the images must contain sufficient feature shapes
and must sufficiently overlap each other, which requires
dense scanning from a number of positions, in order toprecisely register the range images using the ICP algorithm.
Another approach that requires no post-processing procedures such as the ICP algorithm can also be considered,
Y. Tobata is with Linx Cooperaton, 1-13-11, Eda Nishi, Aoba-ku, Yokohama, 225-0014, JAPAN tobta@irvs.is.kyushu-u.ac.jp
Ryo Kurazume, Yumi Iwashita, and Tsutomu Hasegawa are with theFaculty of Information Science and Electrical Engineering, Kyushu
University, 744, Motooka, Nishi-ku Fukuoka, 819-0395, JAPANkurazume@ait.kyushu-u.ac.jp

978-1-4244-9317-3/10/$26.00 © 2010 IEEE

which involves the precise identification of the position of
the range sensor at each measurement. Since this method canobtain the transformation matrix from the sensor specific coordinate system to the global co-ordinate system, local range
images are converted directly into the global co-ordinate
system with a simple transformation calculation. For thisapproach, we have proposed a 3-D measurement system of
the environment using a group of mobile robots and an onboard laser range finder[5]. The proposed system uses the
Co-operative Positioning System (CPS)[6], [7] for multiple
robots, which has been proposed as a highly precise position
identification technique for mobile robots. The automatic
measurement system of the environmental model is realized
by combining CPS and an on-board laser range finder. Thissystem can construct a 3-D model of the environment without
any post-processing procedure such as the ICP algorithm or
manual intervention. In addition, it is possible to register
range images even if the number of measurements is few
and the data is sparse, e.g., if the range images containinsufficient feature shapes or overlapping regions. It is also
possible to construct a 3-D model in environments where
GPS is not available, such as inside structures or in an indoor
environment.
This paper proposes the extension of the proposed system[5] for an automatic 3-D laser measurement system. The
measurement procedure is planned manually and automatically based on the partially measured structure of theenvironment. Planning and measurement experiments by themobile robots are successfully carried out. In Section 2,
an overview of the previous approaches will be presented.
Section 3 will present a short introduction to CPS, which is
the highly precise positioning technique for mobile robots
in unknown environments, while Section 4 will give a
detailed description of the proposed system, In section 5, pathgeneration experiments for mobile robots according to thepartially measured environmental model will be described.
II. R ELATED WORK
The proposed system is related to the Simultaneous Localization And Mapping (SLAM) method[8], [9], [10], [11],[12], [13], [14], [15], which has attracted a great deal ofattention in the robotics community. The 3-D model obtainedby the proposed system can be refined by applying ICP.
Obviously, the refined measurement position from ICP canalso be fed back to the positioning system. This closed-loop
control will increase the accuracy of both the 3-D model andthe robot position.

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In SLAM, the map is created by performing two processes
simultaneously, localization of the robots and measurementof the surrounding environmental information. Thus, the following two elements are essential to create a high-accuracy
environmental map with the SLAM system.
(1) High accuracy self-localization system(2) High accuracy measurement system for surrounding
environment
There are several issues to be considered for Item (1). The
self-localization method that has been proposed until now, forexample, odometry, does not have the ability to measure theself-location of a mobile robot with high accuracy in a bumpy
area or in an environment with pitch differences. The SLAM
system, which sequentially identifies the location of a mobile
robot based on the previous positioning record, requires
the characteristic features of the environment and has thesame problems as odometry, which is the accumulation of
measurement error caused by the measurement device. To
reduce error accumulation, loop detection and refinementof the obtained models and paths are the principal, criticalissues in SLAM.
Concerning Item (2), systems using a laser range finder
are effective and often used from the point of view of costand accuracy. Originally, laser range finders were large-scale,expensive, and intolerant of vibrations. However, recently,
smaller and more inexpensive laser range sensors have beendeveloped and are readily available.
A system that obtains high-accuracy environmental datawith laser range finders is used not only in robotics but
also in other fields, such as in creating digital archivesof cultural heritage. For example, some projects to saveaccurate 3-D images of vanishing precious cultural heritage
as digital data are being carried out at the Angkor monument
in Cambodia[16], the Forbidden City in China[17], and theArchaeological Area in Pompeii, Italy. For these projects,
high accuracy laser measurement devices or digital cameras
are used.
III. C O - OPERATIVE 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 co-ordinate system
by a simple co-ordinate transformation calculation.
Several position identification methods have been proposed, and these methods can be classified into four categories:(1) Integrate 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) Use the Simultaneous Localization and Mapping(SLAM) method.

Method (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 3-D positioning including elevation is
impossible. Method (2) has high accuracy if the 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 has recently become very
popular, especially for field robots. However, this method
also has several drawbacks. For example, active fields ofrobots are limited to an outdoor environment with an unobstructed view of satellites, and the accuracy is currently not
so high, unless the special techniques described in Section
1 are used. Method (4) is based on matching sequentiallyobserved local features acquired by external sensors such as
laser range finders or cameras. When considering the features
as landmarks, this constitutes a general case of method(2). Although this method is a prospective technique for
precise position identification in unknown and unstructured
environments, precise and sufficient information between
consecutive observations must be acquired through dense
measurements.
To overcome these limitations for the position identification problems and achieve accurate positioning of mobile
robots, Kurazume et al. proposed the Co-operative Positioning System (CPS)[6]. In this system, multiple robots withhighly precise measurement devices for their mutual positions are controlled co-operatively. This can lead, compared
to conventional positioning techniques, to high positioning
accuracy, even in unknown and uneven environments.
The basic principle of CPS is as follows: Divide the robots
into group A and group B. Group A remains stationary andacts as a landmark while group B moves. Group B thenstops and acts as a landmark for group A. This alternating
behaviour is repeated until the target position is reached.
By using the concept of ”portable landmarks,” CPS has a far
lower accumulation of positioning errors 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, 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.

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(3) The position of child robot 2 is identified in the samemanner as in 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, a position
calculation that suppresses error accumulation is rather
complicated[7]. In CPS, although the accuracy is quite
high, measurement errors are gradually accumulated withthe characteristics of the errors depending on the moving
histories of the robots. To minimize error accumulation bytaking the moving histories into account, a nonlinear leastsquared method based on the sequential estimation of error
covariance matrices is proposed. The performance of CPS
has been tested through many experiments in a variety ofenvironments, such as an unpaved outdoor environment withsteep slopes, and confirmed that, compared to odometry,
highly precise robot tracking is achievable [7].

Parent robot

2-axis attitude sensor

Parent robotzx

Fig.2(c)). The parent robot is equipped with an on-board
2-D laser range finder (LMS 200, Sick), a high-precision
two-axes altitude sensor (MD900T, Applied Geomagnetics),
and a total station for the survey (GTS-9005A, TOPCON
Ltd.)(Table I), which is used to measure the relative positionsof the child robots. Even if the body is tilted on a slope, thebody inclination is compensated by an altitude sensor, andprecise positioning of the robots is achieved. The 2-D laser
range finder can acquire 2-D slit-like range data within therange of 80 m and 180 °, as shown in Table II. The parent
robot has a rotating table on the top of its body, and byrotating the table around the vertical axis while scanning
using the 2-D laser range finder, 360◦ 3-D range images asshown in Fig.3 are acquired in 37.8 s.

y

1

1

r1

0

0

2

Child robot 2

ψ1

φ1

2

Child robot 1(a) Fifth CPS machine model, CPS-V

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

(1) Robot 1 and 2 move.

Yaw

1
2 r
2

ψ2

0

0
2

φ2

r2
φ2

ψ2

r1
ψ1

1

Total station,
GTS-825A

2-D scanninglaser range finder,
LMS200φ1

Corner cubes

Parent mobile
unit, P-cle

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

Fig. 1.

(4) Robot 0 moves andmeasures the positionof robots 1 and 2.

Battery

Celeron
650 MHzDC servomotor(36 W)

Co-operative Positioning System (CPS)(c) Child robot(b) Parent robot
Fig. 2.

IV. C ONSTRUCTION OF A 3-D ENVIRONMENTAL MAP

CPS-V is composed of two types of robots

USING MULTIPLE ROBOTSThis section presents a new measurement system for the
precise construction of a 3-D 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 3-D laser range finder fromseveral viewpoints. Each measurement position is precisely
identified by CPS using a parent robot and two child robots.
First, the fifth CPS machine model, called CPS-V, whichis equipped with a 2-D laser range finder and a scanningmechanism, is introduced, and the experimental results forthe construction of indoor and outdoor environmental maps
by CPS-V are given.
A. Fifth CPS machine model (CPS-V)
Figure 2 (a) shows the fifth CPS machine model, CPSV. This system consists of a parent robot (P-cle, Parent
mobile unit, Fig.2(b)) and two child robots (HPI Japan,

Fig. 3.

Range data obtained in one scan

B. Experiment to construct a map of large-scale urban
district
Using the proposed system, an urban district environmental map of the Fukuoka Island City area, which is located in
the Higashi-ku district of Fukuoka City, Japan, was created.
The experiment environment consists of a house and a park,

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TABLE I
S PECIFICATIONS OF THE TOTAL STATION , GTS-9005A
GTS-9005A (TOPCON Ltd.)Range
1.3 ∼ 2,200 m
0.2 mmResolution (distance)
Resolution (angle)3”± 2+2 ppm mm
Precision (distance)±5”
Precision (angle)

TABLE IIS PECIFICATION OF THE LASER RANGE FINDER , LMS200
LMS 200 (SICK Corp.)
Range
80 [m]
180◦
Field of view
Resolution (distance)
10 [mm]0.5◦
Resolution (angle)(a)Fig. 5.

where the difference in height is approximately 2 m. Someof the results are shown in Figs.4 and 5. In this experiment,
the parent robot measured its surroundings at 45 different
viewpoints and moved around up to 115.8 m in x-direction
and 131.8 m in the y-direction while traveling a total of 543.4
m. Compared to the 44 movements of the parent robot, thechild robots traveled and changed their locations 8 times. The
experiment shows that the proposed system has the ability
to create a precise 3-D environmental map, even in a large
outdoor environment.
Initial position
100 m

Final position(45th scanning position)

it would be difficult to plan a safe and global path dueto the lack of depth information. On the other hand, thepartially constructed 3-D map obtained by the proposedsystem provides useful information for searching a next
measurement position and the pathway to the point. Since thepartial architectural structures have been obtained in 3-D, theoperator can freely observe the range images from arbitrary
viewpoints and figure out the optimal path intuitively. The
following sections introduce a path generation experimentusing partially obtained 3-D models and an automatic 3-D
measurement experiment.
A. Path generation experiments using a partially obtained
3-D model

Hill(2 m high)
100 m

Path of parent robot

Fig. 4.

(b)Comparison of actual environment with constructed 3-D map

Obtained 3-D environmental map (overall view)

V. PATH GENERATION EXPERIMENT FOR AUTOMATIC 3-D
MEASUREMENT SYSTEM

In the experiments in Section IV-B, the measurement
points for the robots were determined previously by a humanoperator, and the robots were directed to move to thosepositions. However this is not always possible. For example,
consider the case in which the proposed system is applied
for the 3-D measurement of the inside of collapsed buildings
after a large earthquake. In this situation, a human operator
would not be able to visually check where to move themobile robots. Even if an on-board video camera is available,

The environment for the path generation experiment isshown in Fig.6. In this experiment, a 2-D grid map and birdview images of the partial 3-D model are presented for theoperator to determine the next measurement points. The 2D grid map represents the structure of the environment by
cutting the 3-D model at constant height from the ground.
The bird-view images of the partial 3-D model are createdbased on the 3-D environmental data acquired so far, as inFigs.9 and 10.
The 2-D grid map is created as follows. At first, the point
data within a certain range of height are extracted fromthe point clouds of the 3-D environmental model. Next, theextracted data are projected to a planar 2-D grid map withuniform squares with sides of 200 mm. The quadtree format,
which has the advantage that less memory is required, isused. The processing time for data conversion is 0.06 s on a
Pentium 4 running at 3.00 GHz. The planar map of arbitraryheight can be generated almost instantaneously.
The parent robot traveled a total of 220 m around thebuilding, within an area measuring 106 m in the x-direction
and 33m in the y-direction. The path of the parent robot
is shown in Fig.7. Figures 8 and 9 present the 3-D environmental map. Figure 11 shows the 2-D grid maps andpath generation procedures based on the maps. In Fig.11, thesquare marks show the current position of the parent robot,

366

and the triangular marks indicate the next target position for
an environmental measurement. In these experiments, thenext target position was determined by a human operator
based on the 2-D grid maps and bird-view images. Although
the paths to the target positions were generated automaticallyby assuming that there was no unobserved obstacles in thepathway, the actual motion of the robots was made in manual
operation for safety reasons.

(a) Actual viewFig. 9.

(b) Obtained 3-D models
Obtained 3-D model

Current position ofparent robot

Fig. 6.

10 m
Bicycle-parking spacey(a) Position 0(b) Position 1(c) Position 2(d) Position 7(e) Position 15(f) Position 19

Experimental environment surrounded by buildings

Building

Initial position(0, 0, 0)x
CliffFinal position(-3.2, -3.5, 0.3)

Wall

Building(-91.5, -23.1, 0.3)
Building

Building: Scanning position of parent robot.

Fig. 7.

(14.5, -26.9, 0.7)

Path of parent robot

The path of the parent robotaround the building

Fig. 10.

The partially observed 3-D model presented for the operator

B. Automatic 3-D measurement experiment

20 m

Fig. 8.

Obtained 3-D environmental map

The next target position of the parent robot is selected sothat the distance from the current position is less than 23 m,
which is the maximum distance for adjacent points in range
data to be less than the grid width. In other words, if an
object is located further than 23 m, some grids covered by
the object are not detected as being in the occupied grid, andthe robot should not move in this area.

The next step is the automatic generation of the target
positions based on the 2-D grid map. Figure 12 shows theindoor environment for automatic generation experiments ofthe target measurement positions. Figure 13 represents thegrid map with squares with sides of 500 mm and a virtual
CPS-V model.
Figure 14(a) shows the 3-D model of the corridor acquiredat the initial position (position 0) and the grid map createdaccording to the point data. The next target position is
selected automatically as follows. The distance from thecurrent position is less than 7 m, which is the reliable
distance of the laser measurement, the position is one of thefurthest points, and there is no obstacle between the point
and the current position. Figure 14(b) shows the result of thelaser measurement after moving to the new position (position
1).
Figure 15 shows the sequential procedure of the measure-367

Child robots
Parent robot

Obstacle Grid

Partial 3-D
environmental map

Estimated path
Current position Next positionof parent robotof parent robot

Obstacle Grid
Next position of parent robot(a) Position 0(b) Position 1(b) Position 1(a) Position 0
Fig. 14.

Calculated next target position

Next position of parent robot

Obstacle Grid(c) Position 2

Parent robot(d) Position 7
Child robots(a) Position 0(e) Position 15
Fig. 11.

(b) Position 1(f) Position 19

Generated 2-D grid map at each parent’s position(c) Position 6
Fig. 15.

Generated Grid-based map and the target position

Child robots

Parent robot

Fig. 12. Experimental environment : corridor

Fig. 13.

2D grid map

ments repeated 6 times. As shown in this figure, the parent
robot automatically repeated CPS-positioning based on thelaser measurements, determined the next target positions, andmoved to the desired position. The parent robot moved 42.2
m in the x-direction and 3.4 m in the y-directions, while
the observed environment from the 7 positions is shownin Figs.16. Figure 17 shows the acquired 3-D map of thecorridor.
VI. C ONCLUSIONS
A new 3-D measurement system, ’CPS SLAM’, wasproposed for large-scale architectural structures. This system
is composed of multiple mobile robots and an on-board laser
range finder that uses a highly precise positioning techniquecalled the Co-operative Positioning System (CPS) to localize
the robots. Measurement experiments in unknown and large
indoor/outdoor environments were successfully carried out
using the newly developed multiple-robot system, called
CPS-V.

The proposed system has the ability to create a 3-D model
without any post-processing procedures. Furthermore, if desired, post-processing procedures, such as the ICP algorithm,
can be optionally applied to the 3-D model constructed by
the proposed system for a subsequent refinement. Therefore,a fully automatic 3-D measurement system for large-scalearchitectural structures can be realized based on the proposedsystem. Useful applications include digital preservation ofculturally important structures and search for survivors inunderground malls or inside of building collapsed during anearthquake. This paper presented some fundamental experiments for fully automatic geometric modeling based on thepartial 3-D model and the 2-D grid map.
In the future, the authors intend to investigate the development of an efficient 3-D laser measurement system thatwill automatically build an optimal measurement plan. The
positioning accuracy of CPS can also be further improvedby back-projection, that is, the observation of some stablelandmarks before and after movement and the correction ofeach measurement position by back-projecting the errors. As
well, a combination of CPS and the ICP method that can not
only be used for refinement of the model, but also for the
improvement of accurate positioning, will be considered.

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