AR City Representation System Based on Map Recognition Using Topological Information.
ABSTRACT This paper presents a system for overlaying D GIS data information such as 3D buildings onto a 2D physical urban map. We
propose a map recognition framework by analysis of distribution of local intersections in order to recognize the area of the
physical map from a whole map. The retrieval of the geographical area described by the physical map is based on a hashing
scheme, which is called LLAH. In the results, we will show some applications augmenting additional information on the map.
- SourceAvailable from: Nadia A Najjar[Show abstract] [Hide abstract]
ABSTRACT: Geovisualization has traditionally played a critical role in analysis and decision-making, but recent developments have also brought a revolution in widespread online access to geographic data and integration tools, particularly for map-based interfaces. This next generation of geovisualization applications is often characterized by high interactivity and strong end-user participation in both development and use. Building the most effective tools to support user-centered geographic visualization faces a significant challenge, though: very little is known about how people interact with maps. To date, map use research has typically focused on higher order use goals or cognitive interpretations of static map representations. Our research employs Human-Computer Interaction approaches in order to investigate user behaviors that contribute to interactive map use and understanding. This paper describes our approach to studying geovisualization interaction and presents our pilot user studies and initial interaction model. By building a better understanding of how people interact with map interfaces, we will be able to design better user-centered geographic visualizations and learn how to best customize these applications to specific user groups.16th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, ACM-GIS 2008, November 5-7, 2008, Irvine, California, USA, Proceedings; 01/2008
R. Shumaker (Ed.): Virtual and Mixed Reality, LNCS 5622, pp. 128–135, 2009.
© Springer-Verlag Berlin Heidelberg 2009
AR City Representation System Based on
Map Recognition Using Topological Information
Hideaki Uchiyama1, Hideo Saito2, Myriam Servières3, and Guillaume Moreau4
1,2 Keio University, 3-14-1 Hiyoshi, Kohoku-ku 223-8522, Japan
3,4 Ecole Centrale de Nantes-CERMA, 1, Rue Noë 44300 Nantes, France
Abstract. This paper presents a system for overlaying 3D GIS data information
such as 3D buildings onto a 2D physical urban map. We propose a map recog-
nition framework by analysis of distribution of local intersections in order to
recognize the area of the physical map from a whole map. The retrieval of
the geographical area described by the physical map is based on a hashing
scheme, which is called LLAH. In the results, we will show some applications
augmenting additional information on the map.
Keywords: GIS, Augmented Reality, LLAH.
Geographical Information Systems (GIS) have become essential tools for studying,
handling and planning urban development. GIS can superimpose layers (representing
homogeneous information) that are fused together to generate maps. GIS data can be
updated any time and are thus more up-to-date than traditional paper maps. They can
moreover be adapted in real time to meet the user's need.
One of the research issues in GIS community is GeoVisualization, which is a way
of designing an interface and displaying and handling the spatial and temporal GIS
data on the interface [1, 2]. The advantages of using Augmented Reality (AR) tech-
niques to display digital information on standard paper maps have been shown,
because AR enables 3D data to be manipulated easier [3, 4, 5]. Moreover, GIS need a
shift towards 3D to be compatible with sustainable development concerns. To manage
increasing complexity of sustainable development requirements, spatial and temporal
queries have to be handled to compute new indicators that are now being defined. For
instance, a thermal comfort indicator could be 'walls that have more than 8 hours
sunlight in winter and less than 2 hours in summer'. Visualizing the results of such a
query requires 3D representation way because sunlight exposure is dependent on
building height and neighboring buildings. 3D virtual environments are not easy to
manipulate for local authorities. That is why we assume that the use of AR maps will
facilitate the display of such results by letting the user manipulate both a paper map
and the viewpoint in a natural way.
In this paper, we propose a framework of map recognition technique to establish a
correspondence between the image of a real map captured with a camera and a GIS.
AR City Representation System Based on Map Recognition 129
Intersections are extracted from the input image, and then matched with the GIS data,
as in the problem of “Document image retrieval” . We are then able to compute the
camera position and orientation with respect to the map assuming that a map is flat,
and display more information from the GIS. In experimental results, we will show
that our framework is compatible with AR system and some applications.
The rest of the paper is organized as follows: we will first briefly present related
works that can be used to match images representing the same objects, i.e. compute
the geometric transformation that links the two images. We will then provide an over-
view of our system in Section.4 and the algorithm in Section.5. Finally, experimental
results will be presented and discussed.
2 Related Works
The problem of finding a match for a query object using feature points has been ad-
dressed in various ways. The feature points can be described using rich descriptors
such as SIFT  or SURF , that typically use image patches. These descriptors are
robust in terms of change of illumination, scale and rotation and describe them with
high-dimensions vectors. The search methods have then to deal with the problems of
nearest neighbor search in high dimensions with approximate nearest neighbor
searching  or locality-sensitive-hashing .
Rich descriptors are well suited to the retrieval of images near-identical to the ones
in the database, with few repetitive texture patterns. By contrast, 2D maps can be pre-
sented in different ways, according to the manufacturer, and the retrieval method
needs then to focus on the geometry of the urban environment they describe. For this
reason, the feature points need to be specific to urban environments and the location
of intersections are used in this paper.
It is not possible to distinguish an intersection query using only the location of a
single intersection. For this reason, the essential information in retrieval is the
arrangement of the features points. Such an arrangement, in our case, must be invari-
ant to the orientation of the camera relative to the map.
One of the recognition methods by geometrical information is Geometric hashing
(GH) . GH is such a general model-based object recognition method widely used
in computer vision as well as in other domains. The introduction of a geometric
invariant yields a computational cost quite important, that is unsuitable for an aug-
mented reality application. A probabilistic reduction of the number of feature points
results in accuracy degradation and has led to the introduction of “Locally Likely
Arrangement Hashing” (LLAH), which outperforms GH in both processing time and
required amount of memory . In this scheme, neighboring points are considered for
the calculation of an affine invariant used as a key in a hashing table. A voting tech-
nique is employed for retrieval, insuring efficiency and robustness against erasure of
feature points. We use a combination of this method and a more traditional tracking
technique to first recognize the area in the camera filed of view, then overlay 3D
buildings in real-time.
130 H. Uchiyama et al.
The user has a hand-held device equipped with a camera coupled with a computer, for
example a cellular phone or a see-through HMD. In our experimental setup, we use a
digital camera and a laptop (Fig.1 (a)).
The physical map can be displayed on a desktop, on a wall or any flat surface. At
the beginning of the use, the camera needs to be in a position more or less parallel to
the map, so that perspective distortion is not too important (Fig.1 (b)). After that, the
user can move more freely (Fig.1 (c)) to watch the 3D buildings and the GIS data in
real-time on the screen of the device (Fig.1 (d)). All physical maps should be regis-
tered in the database beforehand. The user can select a map from the registered map
for watching its visual aids.
(c) Input (d) Output
Fig. 1. System Overview
In the off-line process, the initial database of LLAH features of all intersections in
GIS is generated beforehand. In the on-line process, the same process is executed at
every frame (Fig.2). From a captured image, intersections are extracted by using sim-
ple color segmentation because their color was determined beforehand. Since another
automatic intersection extraction method has been proposed , we focus on
map image retrieval by distribution of intersections. For each intersection, the
AR City Representation System Based on Map Recognition 131
corresponding intersection is retrieved from the database by using the LLAH features.
Based on the number of the retrieved intersections, the area of the map can be deter-
mined. In addition, the camera pose can be computed by using the retrieved intersec-
tions for displaying 3D GIS data of the area. At the same time, LLAH features of each
intersection in the captured image are updated.
Fig. 2. Algorithm
4.2 GIS Data
Real GIS data of a large French city is used. GDMS  is used to process the data in
with a simple query, all intersections are extracted from the road network to
build the features points that are used in the method.
following Neubauer and Zipf's idea , we have built an XML style file that
describes how the GIS database will be rendered in the virtual environment, i.e.
whether a polygon layer should be rendered with flat surfaces or extruded poly-
gons, and additional information such as the color to use. We have thus built a
VRML builder above GDMS that transforms GIS data according to the XML
file and generates a VRML file.
The area described by the data can in theory be very large, and must be sub-divided in
sub-areas that correspond to the size of the physical maps used as queries. These
sub-areas are defined by a specific ID with intersection’s IDs such as (Area ID, Inter-
section ID1, Intersection ID2 ...). Each Intersection is stored with its belonging area
such as (Intersection ID, Area ID). Additional information of the map such as 3D
models of buildings is also tagged with its belonging area ID to be able to retrieve the
information from area ID.
Camera Pose Estimation
132 H. Uchiyama et al.
Fig. 3. Matching by LLAH
Fig. 4. Outlier Removal by RANSAC based Homography Computation
4.3 Intersection Recognition
From a captured image, red intersections are extracted by finding red region. For each
intersection’s region, the center is computed. Based on LLAH , the corresponding
intersection of each extracted intersection is retrieved from the database. As a result,
some intersections are correctly matched and other intersections are wrongly matched
(Fig.3). Since there are similar arrangements of intersections, the result of LLAH
sometimes includes wrongly matched intersections. For removing these wrongly
matched intersections, we use RANSAC based homography computation .
Since the map is 2D, the correspondence between the map in the database and the
map in a captured image can be described by homography. For computing a homo-
graphy, several intersections are randomly selected and evaluated in the RANSAC
process. After that, high confidential intersections are selected (Fig.4).
After the homography is computed, the homography can be converted into a
camera position and orientation , which is equivalent to camera pose estimation.
4.4 Database Update
Since the initial database is generated by using intersections in GIS, we can say that
the LLAH features in the initial database are generated by using a top view image. If
AR City Representation System Based on Map Recognition 133
we use only the initial database, the retrieval of intersections will succeed in case of
near top view including many points. By adding new LLAH features according to the
changes of the user's viewpoint, the retrieval will still work when the captured image
is not close to the top view image.
When the homography is computed in Intersection Recognition, the intersections
in the database can be reprojected onto the captured image by using the homography.
If a distance between the reprojected intersection and an extracted intersection in the
image is within a threshold, the extracted intersection is matched with the reprojected
intersection. Thanks to the reprojection, many intersections which are not matched
with corresponding intersections in GIS by LLAH can be matched.
5 Experimental Results
5.1 Computational Costs
For evaluating computational costs of AR display, 100 frames are captured in order to
compute the average computational costs. Our device is composed of a laptop (Intel
Core 2 Duo 2.2GHz and 3GB RAM) with a firewire camera.
The computational costs of Intersection Recognition and Database Update depend
on the number of extracted intersections, which can be represented by O(N) in the
case that the number of extracted intersections is N. In our algorithm, total computa-
tional costs are 46 msec (more than 20fps). However, 3D Model Rendering took most
computational costs because GIS data includes detailed polygons. The content should
be appropriately selected depending on computational costs.
Table 1. Computational Costs
Process Time (msec)
Camera Pose Estimation
3D Model Rendering
Since the camera pose against the map is estimated, any virtual object can be overlaid
at an appropriate position. In this section, we will introduce one application for AR
Fig.5 shows a system for displaying a picture at the captured place on the map. If a
user takes a picture with its information of the captured place and input it into the
application, the application displays the picture at the captured place on the map
(Fig.5 (b)). The user can recognize the places and their relationships where the user
134 H. Uchiyama et al.
(a) Input (b) Tagged images
Fig. 5. Display of tagged images on a map
In this paper, we have presented an AR representation system for 3D GIS that are
based on the augmentation of a physical map including intersections. It provides a
natural device for 3D GIS information representation and manipulation. Intersection
recognition is based on LLAH framework by using geometrical relationship with
neighbor intersections. For free camera moving, update of LLAH features is adapted.
Our future work will be centered on two main topics. First, we will be using a real
physical map, easier to manipulate, but requiring more image processing to recover
the features needed in the initialization phase. Second, a map contains more informa-
tion than just intersections, and this could be used to extract other features such as
This work is supported in part by a Grant-in-Aid for the Global Center of Excellence
for high-Level Global Cooperation for Leading-Edge Platform on Access Spaces
from the Ministry of Education, Culture, Sport, Science, and Technology in Japan.
1. Wilson, D.C., Lipford, H.R., Carroll, E., Karr, P., Najjar, N.: Charting new ground: model-
ing user behavior in interactive geovisualization. In: Proc. the 16th ACM GIS (2008)
2. Wood, J., Dykes, J., Slingsby, A., Clarke, K.: Interactive visual exploration of a large spa-
tio-temporal dataset: Reflections on a geovisualization mashup. IEEE Trans. VCG 13,
3. Romao, T., Dias, E., Danado, J., Correia, N., Trabuco, A., Santos, C., Santos, R., Nobre,
E., Camara, A., Romero, L.: Augmenting reality with geo-referenced information for envi-
ronmental management. In: Proc. the 10th ACM GIS (2002)
4. Hedley, N.R., Billinghurst, M., Postner, L., May, R., Kato, H.: Explorations in the use of
augmented reality for geographic visualization. Teleoperators and Virtual Environ-
ments 11, 119–133 (2002)
AR City Representation System Based on Map Recognition 135
5. Reitmayr, G., Eade, E., Drummond, T.: Localisation and interaction for augmented maps.
In: Proc. ISMAR, pp. 120–129 (2005)
6. Iwamura, M., Nakai, T., Kise, K.: Improvement of retrieval speed and required amount of
memory for geometric hashing by combining local invariants. In: Proc. BMVC, pp. 1010–
7. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110
8. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis,
A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer,
9. Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.: An optimal algorithm for
approximate nearest neighbor searching fixed dimensions. J. of the ACM 45, 891–923
10. Datar, M., Indyk, P., Immorlica, N., Mirrokni, V.S.: Locality-sensitive hashing scheme
based on p-stable distributions. In: Proc. SCG, pp. 253–262 (2004)
11. Lamdan, Y., Wolfson, H.: Geometric hashing: A general and efficient model-based recog-
nition scheme. In: Proc. ICCV, pp. 238–249 (1988)
12. Chiang, Y.Y., Knoblock, C.A.: Automatic extraction of road intersection position, connec-
tivity, and orientations from raster maps. In: Proc. ACM GIS (2008)
13. Bocher, E., Leduc, T., Moreau, G., Cortés, F.G.: Gdms: An abstraction layer to enhance
spatial data infrastructures usability. In: Agile 2008 (2008)
14. Neubauer, S., Zipf, A.: Suggestions for extending the OGC styled layer descriptor (SLD)
specification into 3D – towards visualization rules for 3D city models. In: Proc. UDMS,
Stuttgart, Germany (2007)
15. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with
applications to image analysis and automated cartography. C. of the ACM 24, 381–395
16. Uematsu, Y., Saito, H.: Vision based registration for augmented reality using multi-planes
in arbitrary position and pose by moving uncalibrated camera. In: Proc. MIRAGE, pp. 99–