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Close-range imaging, long-range vision

Table 1. Specification of omnidirectional Camera

Focal Length 5.9 [mm]
Image Circle ¢ 18[mm]
Image Size 3032x2008 [pix]
Shutter Interval 2 [sec/frame]
Color Depth 24/32[bit/pix]

Figure 3. Example of omnidirectional image
2.3 Real-time positioning by D-GPS and GPS interpolation
When using GPS equipment in an urban area, the quality of
positioning can drop due to shielding and multi-path effects
caused by buildings and other objects. In recognition of such
problems, we have adopted a D-GPS device having a multi-
path-elimination function. In addition, to deal with shielding
effects that not even GPS equipment with the above function
can eliminate, we have also developed for our system a GPS
interpolation device that employs a gyro sensor and speed
sensor. A key feature of this device is its ability to perform
positioning by interpolating in real time while directly inputting
data from GPS equipment and monitoring the quality of GPS
data. Figure 4 shows an example of actual position data for a
certain area. For comparison purposes, the figure also shows
typical position data from GPS equipment. Specifically, the
solid circles indicate typical GPS position data with no
interpolation while the empty circles indicate position data
obtained by our system. These circles draw a curve initially
moving downward from the left and then changing direction
and moving upward to the right. Examining this data, we see
that typical GPS position data is missing in the "lacking
section" while position data by our system features interpolated
data in the “interpolated section." In addition, typical GPS
position data includes disorder as a result of poor quality data in
the “noisy area." The results demonstrate that our mobile
mapping system can obtain high-accuracy positional
information in a stable manner even under conditions that
prevent positioning by GPS.

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Figure 4. Comparison our system's position data with typical D-
GPS data
2.4 Synchronization of positional information and
omnidirectional images
If mistakes are made when assigning correspondence between
omnidirectional images and positional information, it will not
be possible to correctly convert reconstructed building models
to global coordinates. It is therefore important to establish strict
synchronization between GPS-based positional information and
captured omnidirectional images. In the former system that we
created, the lag between shooting time and positioning time was
a maximum of 8 seconds due to Windows Operating System
delays as a result of image-storage processing. In our current
system, however, the positioning computer records the shutter
opening time of an omnidirectional camera in GPS time. As a
result, the lag between shooting time and positioning time has
been reduced to within 0.5 second in our system.
Figure 5 shows the process flow from image input to building-
model reconstruction. In this process, the system first
determines camera attitude with respect to a captured image by
estimating the focus of expansion (FOE) position within the
omnidirectional image. Then, with respect to the images
corrected for camera attitude, the system extracts and tracks
feature points targeted for measurement and calculates their
three-dimensional positions by stereo measurements. Next, the
system configures surfaces using the calculated three-
dimensional points and reconstructs building shape. Finally, the
system re-projects the reconstructed shape onto the building's
lateral omnidirectional image and extracts texture to reconstruct
the building model.
The following describes the above process in more detail.

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