Full text: Close-range imaging, long-range vision

S. Ozawa', I. Miyagawa*, K. Wakabayashi’, T. Arikawa” 
“NTT Cyber Space Laboratory, NTT Corporation, 1-1 Hikarinooka Yokosuka-Shi Kanagawa, 239-0847, Japan 
Commission V, WG V/2 
KEY WORDS: Mapping, Vision, Urban, Building, GPS, Virtual Reality, Mobile, Image 
We have developed a mobile mapping system for the purpose of acquiring building models having three-dimensional information 
such as building shape and position in urban space. The system mounts two omnidirectional cameras and position-sensing equipment 
on a vehicle and obtains images and positional information in a synchronous manner. It features three major functions. The first is a 
GPS interpolation function that enables positioning even at places in an urban environment where signals cannot be received from 
GPS satellites. The second is a synchronization function that synchronizes positional information and omnidirectional images and 
enables reconstructed models to be accurately converted to global coordinates. The third is a shutter control function that enables the 
shooting intervals of the two omnidirectional cameras to be either synchronized or non-synchronized. These functions make it 
possible to select images for all sorts of baselines from multiples images captured by the two omnidirectional cameras and to make 
stereo measurements for an optimal baseline according to the height of the target building and its distance from the camera. This 
system can perform high-speed data acquisition at a rate of four square kilometers per hour. 
Three-dimensional (3D) digital maps that contain building- 
related information such as position and shape in urban space 
are eagerly awaited in a wide range of fields (Teller, 1999; 
Virtual Helsinki, 1999). 
In particular, 3D digital maps can be used as an important 
information platform in fields like Intelligent Transportation 
Systems (ITS) and Geographic Information Systems (GIS), and 
they can be applied to the simulation of radio-wave propagation, 
disaster countermeasures, etc. They are also expected to find 
use in the entertainment field such as in video-games. 
In short, there is an extremely broad range of needs for 3D 
digital maps. In this regard, not only will the demand for 
accurate shape measurement and high-quality texture intensify, 
but the acquisition of such information will also have to be 
automated and the cost of acquisition lowered. 
Against this background, various techniques have been 
propesed for acquiring urban models by the airborne approach 
using aerial photographs, laser rangefinders, and the like. The 
airborne approach has a major advantage in that a wide area can 
be efficiently reconstructed. For this reason, even our research 
group has been active in this area, researching a technique for 
reconstructing building shape from aerial images and a 
technique for reconstructing building shape from laser range 
data (Miyagawa, 2000; Horiguchi, 1999). 
The airborne approach, however, lacks information on the 
sides of buildings and cannot support the use of building models 
as seen from the ground such as for “walkthroughs.” On the 
other hand, there has been much activity in the research of 
“mobile mapping” that mounts cameras and range sensors on a 
vehicle to reconstruct building shape (Ellum, 2002; GIS 
Development, 2000; Tamura, 1998). Not only can mobile 
mapping reconstruct the sides of buildings, it can also acquire 
data in a relatively short time by utilizing key properties of 
With the above in mind, we set out to develop a mobile 
mapping system for the purpose of automatically reconstructing 
urban building models by installing cameras on a vehicle and 
making mobile measurements. 
The high-accuracy acquisition of positional information is 
essential to mobile mapping, and to this end, the Global 
Positioning System (GPS) is often used. In urban areas, 
however, the quality of position data may drop due to shielding 
by buildings, to multi-path effects, etc, and many mobile 
mapping systems have come to incorporate a mechanism for 
interpolating GPS position data. For our mobile mapping 
system, we have developed and incorporated a function that 
interpolates GPS position data in real time while monitoring the 
quality of GPS positioning. 
Various techniques have been proposed for reconstructing 
shape including a sensor-based technique using range sensors 
(Zhao, 2000) and an image-based technique using captured 
images (Uehara, 2000). The sensor-based technique can directly 
acquire the shape of a building but cannot obtain building 
texture. The image-based technique, on the other hand, has the 
advantage of being able to acquire shape and texture 
simultaneously. A conventional camera, however, has a limited 
angle of view, which means that the range of measurement will 
likewise be limited. Such a camera is therefore not conducive to 
an urban area that wills more than likely feature buildings of 
various heights. For this reason, we have adopted in our system 
an omnidirectional camera that can capture a 360 degree image 
at one time. 
An omnidirectional camera may be of the mirror-projection 
type, which captures an image projected on a mirror with a 
camera, or of the lens type that uses a fish-eye lens. (Kawasaki, 
2001). The mirror-projection type enables the mirror shape to be 
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