This slide is presented in CMC2012 (2012 4th International Conference on Communications, Mobility, and Computing). Abstract. This research presents the development of a mobile AR system which realizes geometric consistency using GIS, a gyroscope and a video camera which are mounted in a smartphone for urban landscape assessment. A low cost AR system with high flexibility is developed. Geometric consistency between a video image and 3DCG are verified. In conclusion, the proposed system was evaluated as feasible and effective.

1. 4th International Conference on Communications,
Mobility, and Computing (CMC2012), Guilin, China
GOAR
GIS ORIENTED MOBILE AUGMENTED
REALITY FOR URBAN LANDSCAPE
ASSESSMENT
TOMOHIRO FUKUDA, TIAN ZHANG, AYAKO SHIMIZU,
MASAHARU TAGUCHI, LEI SUN and NOBUYOSHI YABUKI
Division of Sustainable Energy and Environmental Engineering,
Graduate School of Engineering,
Osaka University, Japan

2. Outline
1. Introduction
2. System Development
1. Development Environment of a System
2. System Flow
3. Verification of System
1. Consideration of allowable residual error
2. Accuracy of geometric consistency with a video image
and 3DCG
4. Conclusion
2

3. Outline
1. Introduction
2. System Development
1. Development Environment of a System
2. System Flow
3. Verification of System
1. Consideration of allowable residual error
2. Accuracy of geometric consistency with a video image
and 3DCG
4. Conclusion
3

4. 1.1 Motivation -1 1. Introduction
 In recent years, the need for landscape simulation has been
growing. A review meeting of future landscape is carried out on a
planned construction site in addition to being carried out in a
conference room.
 It is difficult for stakeholders to imagine concretely such an image
that is three-dimensional and does not exist. A landscape
visualization method using Computer Graphics (CG) and Virtual
Reality (VR) has been developed.
 However, this method requires much time and expense to make a
3D model. Moreover, since consistency with real space is not
achieved when using VR on a planned construction site, it has the
problem that a reviewer cannot get an immersive experience.
4
A landscape study on site VR caputure of Kobe city

5. 1.1 Motivation -2 1. Introduction
 In this research, the authors focus Augmented Reality (AR) which
can superimpose an actual landscape acquired with a video
camera and 3DCG. When AR is used, a landscape assessment
object will be included in the present surroundings. Thereby, a
drastic reduction of the time and expense involved in carrying out
3DCG modeling of the present surroundings can be expected.
 A smartphone is widely available on the market level.
Sekai Camera Web Smartphone Market in Japan
http://sekaicamera.com/ 5

6. モバイル型景観ARの進化 Introduction
1.2 Previous Study 1.
 In AR, realization of geometric
consistency with a video image of
an actual landscape and CG is an
important feature
1. Use of physical sensors such as GPS
(Global Positioning System) and gyroscope. To
realize highly precise geometric
consistency, special hardware which is
expensive is required.
Image Sketch (2005)
2006 6
©2012 Tomohiro Fukuda, Osaka-U

7. 1.2 Previous Study 1. Introduction
2. Use of an artificial marker. Since an artificial marker needs to be always
visible by the AR camera, the movable span of a user is limited. Moreover,
to realize high precision, it is necessary to use a large artificial marker.
Yabuki, N., et al.: 2011, An invisible height evaluation
system for building height regulation to preserve good
landscapes using augmented reality, Automation in
Construction, Volume 20, Issue 3, 228-235.
artificial marker
7

8. 1.3 Aim 1. Introduction
 In this research, GOAR (GIS Oriented Mobile AR) system which realizes
geometric consistency using GIS to obtain position data instead of GPS
which obtains a low accuracy of the location information, a gyroscope and
a video camera which are mounted in a smartphone is developed.
 A low cost AR system with high flexibility is realizable.
(Virtual Object for
Landscape Simulation)
8

9. Outline
1. Introduction
2. System Development
1. Development Environment of a System
2. System Flow
3. Verification of System
1. Consideration of allowable residual error
2. Accuracy of geometric consistency with a video image
and 3DCG
4. Conclusion
9

10. 2. System Development
2.1 Development Environment Of a System
 Standard Spec Smartphone: GALAPAGOS 003SH (Softbank Mobile Corp.)
 Development Language: OpenGL-ES（Ver.2.0），Java（Ver.1.6）
 Development Environment: Eclipse Galileo（Ver.3.5）
 Location Estimation Technology: GIS includes Google Maps API and Digital
Elevation Model (DEM) which is 10 m mesh size
Video Camera
Spec of 003SH
OS Android™ 2.2
Qualcomm®MSM8255
CPU
Snapdragon® 1GHz
ROM：1GB
Memory
RAM：512MB
Weight ≒140g
Size ≒W62×H121×D12mm
Display Size 3.8 inch
Resolution 480×800 pixel
003SH
10

11. 2.2 System Flow -1 2. System Development
While the CG model realizes
Calibration of a video camera
ideal rendering by the
Definition of landscape assessment 3DCG model perspective drawing method,
rendering of a video camera
Activation of AR system produces distortion.
Selection of 3DCG model
Starting of Activation of Activation of
Google Maps gyroscope video camera
Input of DEM
Angle information Capture of live
acquisition video image Distortion Calibration
Position
information
acquisition
Definition of position and angle
information on CG virtual camera
Superposition to live video image and 3DCG model
Display of AR image
Save of AR image
Calibration of the video camera
11
using Android NDK-OpenCV

12. 2.2 System Flow -2 2. System Development
3DCG Model
Calibration of a video camera
Definition of landscape assessment 3DCG model
Geometry, Texture, Unit
Activation of AR system
Selection of 3DCG model
3DCG model allocation file
Starting of Activation of Activation of
Google Maps gyroscope video camera
Input of DEM
Angle information Capture of live
acquisition video image 3DCG model name, File name,
Position Position data (longitude, latitude,
information
acquisition orthometric height), Degree of
rotation angle, and Zone
Definition of position and angle number of the rectangular plane
information on CG virtual camera
Superposition to live video image and 3DCG model 3DCG model arrangement information file
Display of AR image
Save of AR image
Number of the 3DCG model
allocation information file, 12
Each name

13. 2.2 System Flow -3 2. System Development
Calibration of a video camera
Definition of landscape assessment 3DCG model
Activation of AR system
Selection of 3DCG model
Starting of Activation of Activation of
Google Maps gyroscope video camera
Input of DEM
Angle information Capture of live
acquisition video image
Position
information
acquisition
Definition of position and angle
information on CG virtual camera
Superposition to live video image and 3DCG model
Display of AR image
Save of AR image
GUI of the Developed System
13

14. 2.2 System Flow -4 2. System Development
Calibration of a video camera
Definition of landscape assessment 3DCG model
yaw
Activation of AR system
Selection of 3DCG model
Starting of Activation of Activation of
Google Maps gyroscope video camera
Input of DEM
Angle information Capture of live
acquisition video image roll
Position pitch
information
acquisition
Definition of position and angle
information on CG virtual camera
Coordinate System of Developed
AR system
Superposition to live video image and 3DCG model
Display of AR image
Save of AR image
14

15. 2.2 System Flow -5 2. System Development
Calibration of a video camera
Definition of landscape assessment 3DCG model
Activation of AR system
Selection of 3DCG model 1.The user tap the current
location on Google Maps
Starting of Activation of Activation of
Google Maps gyroscope video camera
Input of DEM
Angle information Capture of live
acquisition video image
Position
information
acquisition
2.The position data (longitude,
Definition of position and angle latitude) on the current
information on CG virtual camera location is obtained
Superposition to live video image and 3DCG model
Display of AR image
Save of AR image
3.Altitude is created using
position data (longitude,
latitude) and DEM
15

16. 2.2 System Flow -6 2. System Development
Calibration of a video camera
Definition of landscape assessment 3DCG model
Activation of AR system
Selection of 3DCG model
Starting of Activation of Activation of
Google Maps gyroscope video camera
Input of DEM
Angle information Capture of live
acquisition video image
Position
information
acquisition
Definition of position and angle
information on CG virtual camera
Superposition to live video image and 3DCG model
Display of AR image
Save of AR image
16

17. モバイル型景観ARの進化
17

18. Outline
1. Introduction
2. System Development
1. Development Environment of a System
2. System Flow
3. Verification of System
1. Consideration of allowable residual error
2. Accuracy of geometric consistency with a video image
and 3DCG
4. Conclusion
18

19. 3. Verification of System
3.1 Consideration of allowable residual error
 The residual error of position (longitude, latitude) occurs by the gap with the
position in which a user does a tap on Google Maps as an actual position.
 When the size of the digital map is maximized on Google Maps, the
distance in the real space of the map is 123 m to the size of a screen
being 78 mm. That is, 1 mm on a screen is about 1.6 m in the real space.
 On the other hand, since a tap is operated with a finger, a residual error
may occur only the width of the finger used for a tap. Since the width of
the finger had individual difference, it was set as 5 mm in this research.
 Therefore, if the scale of a digital map and the error of the width of a
finger are taken into consideration, an error will be set to less than 8 m
when directing latitude and longitude.
 Moreover, about the residual error of
altitude, it is expected that 10m mesh
DEM cannot respond to change of the
altitude from a model creation time and
a difference with reality may occur
since the altitude between the mesh 5mm (Width of finger)
= 8m (Distance in real space)
vertices are linearly interpolated.
1mm (Size of screen)
= 1.6m (Distance in real space) 19

20. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG -1
 Experimental Methodology
▶ The parameters for realizing geometric consistency are:
▶ Position: latitude, longitude, altitude by GIS
▶ Angle: yaw, pitch, roll by gyroscope
▶ The accuracy of geometric consistency is determined by
combining the residual error of these parameters.
▶ A known building and viewpoint place are set up.
▶ In one experiment, only one parameter was acquired from a
device and the remaining parameters set up a known value as a
fixed value.
▶ Calculation of residual error between live video image and CG at
the same point
20

21. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG -2
 Known Building Target
▶ GSE Common East Building at Osaka University Suita Campus
▶ W29.6 m, D29.0 m, H67.0 m
Photo Drawing
28.95m 29.6m
28.95m
29.6m
64.8m
64.8m
29.6m
Latitude, Longitude, Orthometric height Outlined 3D Model
34.823026944, 135.520751389, 60.15 21

22. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG -3
 Known Viewpoint Place
▶ No.14-563 reference point. Distance from the reference point to the center
of the Building was 203 m.
▶ AR system was installed with a tripod at a level height 1.5m.
Reference
Point
Building Target
BC
10m A D
203m
Maximum Altitude: 53.5m
Altitude of Reference
Viewpoint Point: 53.1m
(No.14-563
Reference Point）
Latitude, Longitude, Altitude Measuring Points of
34.82145699, 135.519612, 53.1 Residual Error
Minimum Altitude: 51.0m 22

23. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG -4
 Parameter Settings of Eight Experiments
Parameter Settings
(S: Static Value = Known value, D: Dynamic Value = Acquired value from a device )
Position Information of Angle Information of
Experiment CG Virtual Camera CG Virtual Camera
Latitude Longitude Altitude yaw pitch roll
No.1 S S S S S S
No.2 D (GIS) D (GIS) D (GIS) S S S
No.3 D (GIS) D (GIS) D (GIS) D D D
1)
No.4 D (GPS) D (GPS) D (GPS) D D D
1) T. Fukuda, T. Zhang, A. Shimizu, M. Taguchi, L. Sun, N. Yabuki, “SOAR: Sensor oriented Mobile
Augmented Reality for Urban Landscape Assessment”, Proceedings of the 17th International
Conference on Computer Aided Architectural Design Research in Asia (CAADRIA), pp.387-396, 2012-4.
23

24. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG
 Calculation Procedure of Residual Error
1. Pixel Error: Each difference between the horizontal direction and vertical
direction of four points measured by pixels (Δx, Δy).
⊿x
⊿ｙ Live Image
CG Model
Calculation image of residual error between live video image and CG
2. Distance Error: From the acquired value (Δx, Δy), each difference in the
horizontal direction and vertical direction was computed as a meter unit
by the formula 1 and the formula 2 (ΔX, ΔY).
(1) (2)
W: Actual width of an object (m)
H: Actual height of an object (m)
x: Width of 3DCG model on AR image (px)
y: Height of 3DCG model on AR image (px)
24

25. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG
 Results: No.1 AR image
Position Information of Angle Information of
Experim
CG Virtual Camera CG Virtual Camera
ent Latitude Longitude Altitude yaw pitch roll
No.1 S S S S S S
(0.12m/pixel)
No.1 No.2 No.3 No.4
Pixel Error
Max. Mean Min.
Unit
Distance Error
Distance Error Unit:
Unit No.1 No.2 No.3 No.4

26. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG
 Results: No.2 AR image
Position Information of Angle Information of
Experim
CG Virtual Camera CG Virtual Camera
ent Latitude Longitude Altitude yaw pitch roll
No.2 D (GIS) D (GIS) D (GIS) S S S
(0.12m/pixel)
No.1 No.2 No.3 No.4
Pixel Error
Max. Mean Min.
Unit
Distance Error
Distance Error Unit:
Unit No.1 No.2 No.3 No.4

27. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG
 Results: No.3 AR image
Position Information of Angle Information of
Experim
CG Virtual Camera CG Virtual Camera
ent Latitude Longitude Altitude yaw pitch roll
No.3 D (GIS) D (GIS) D (GIS) D D D
(0.12m/pixel)
No.1 No.2 No.3 No.4
Pixel Error
Max. Mean Min.
Unit
Distance Error
Distance Error Unit:
Unit No.1 No.2 No.3 No.4

28. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG
 Results: No.4 AR image
Position Information of Angle Information of
Experim
CG Virtual Camera CG Virtual Camera
ent Latitude Longitude Altitude yaw pitch roll
No.4 D (GPS) D (GPS) D (GPS) D D D
(0.12m/pixel)
No.1 No.2 No.3 No.4
Pixel Error
Max. Mean Min.
Unit
Distance Error
Distance Error Unit:
Unit No.1 No.2 No.3 No.4

29. 3. Verification of System
3.2 Accuracy of geometric consistency with a video image
and 3DCG
 Allowable residual error of longitude and latitude: 8m at the
maximum
 Result of No.3, the maximum residual error is 6.5 m, a mean
distance error is 2.2 m, and it became smaller than anticipation.
 When the mean distance error of No.3 was compared with No.4:
 Horizontal: 0.7 m larger
 Vertical: 5 m smaller
 Proposed GIS technique obtains position data on higher accuracy especially in a
vertical direction rather than GPS.
Max. Mean Min.
6.3m
Distance Error Unit:
3m 2.3m
1.1m 1.3m 1.3m
0.11m
No.1 No.2 No.3 No.4
No.1 No.3 No.4
29

30. Outline
1. Introduction
2. System Development
1. Development Environment of a System
2. System Flow
3. Verification of System
1. Consideration of allowable residual error
2. Accuracy of geometric consistency with a video image
and 3DCG
4. Conclusion
30

31. 4. Conclusion
4.1 Conclusion
 The developed AR system has geometric consistency using GIS and the
gyroscope with which the smartphone is equipped. Therefore, a user can use
it easily and we can describe it as a system with high flexibility.
 In GOAR system, appearance of the residual error of longitude and latitude
by a user specifying a current position on Google Maps and the residual error
of altitude by using 10m meshed DEM is expected. As a result of the
experiment, the maximum residual error of longitude and latitude was 6.5 m,
and the mean distance error was 2.2 m. The maximum residual error of
altitude was 2.6 m and the mean distance error was 1.3 m. Any result
became smaller than assumption.
 Consequently, the proposed GOAR system was evaluated as feasible and
effective.
31

32. 4. Conclusion
4.2 Future Work
 A future work should attempt to reduce the residual error included in the
dynamic value acquired with gyroscope.
 It is also necessary to verify accuracy of the residual error to objects
further than 200m away and usability.
32

33. Thank you for your attention!
E-mail: fukuda@see.eng.osaka-u.ac.jp
Twitter: fukudatweet
Facebook: Tomohiro Fukuda
Linkedin: Tomohiro Fukuda