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2 MAKING LAYOUT PLAN OF A ROAD INTERSECTION
For road planning or reparation, it is often
necessary to accurately find the various features of
the road such as manhole, road signs, stop-line,
crossing lines etc. So, we tested our device to
make an accurate layout plan of the road
intersection with our DI-1000, GPT and FC-10A.
We connected DI-1000 and GPT on-line. Then we
measured and made orientation and supplement
measurement to produce an ortho-image. After that
we applied our software DI-1000 and FC-10A
(Topcon Digital Plain Table software), which are
both installed in the same Pen-Computer, on the
obtained ortho-image to produce the finale layout
plan of the road intersection.
For photographing we used RDC5000 (Ricoh CCD
Camera) of 2.3 million pixels. We got on the
stepladder of 2.5m and took picture of the area of
about 40m X 15m. We took two pictures with
slightly different perspective (See: Figure 5 right
below). And on GPT-1002 we determined 6 main
targets which are different for two photos and 30
supplemental targets altogether, which makes 42
targets in total.
The figure 5 above shows what came out of
superimposing the ortho-image, which had been
obtained from two pictures with DI-1000, on the
image, which had been produced out of the ortho-
image by FC-10A. The figure 5 below is its flat
layout plan.
Since the object of survey is a two-dimensional
road intersection we did not use stereo-photography.
The accuracy of the image depends on the degree
of image-resolution and the accuracy of GPT
measurement. It is = 5cm. For the map of scale
1/500 this would be sufficient for practical purposes. Figure 5. Making layout plan superimposing Ortho-image
3 THREE DIMENSIONAL MEASUREMENT
To grasp and find the way to tackle with the dangerous spot of land, for example, we must first find out its three-
dimensional features by gathering the basic data necessary for analyzing the situation.
However, where the land is steep it is difficult to grasp the actual facts. Besides, up to now, even if the immediate action
was necessary, we could not but spend a lot of time (more than a week) to gather necessary basic data and facts.
Or again, when the object is irregular and complex, we need to have multiple three-dimensional data in order to make
an accurate ortho-image. If we use GPT it is not difficult to obtain the measurement points, but unfortunately it takes
time to get measurement of each point. Besides, some inaccuracy could creep in, as it is actually impossible to make
physical access to the points. To overcome such inconveniences, we have now developed a software to easily obtain an
accurate ortho-image. Using the several measurement points obtained from GPT as initial values, we can automatically
measure from the stereo images it produces.
In order, therefore, to investigate the problems and to test the accuracy of stereo-matching with GPT (T IN), we made a
simulation experiment of measuring three-dimensional object.
In this experiment we chose a cliff side and placed 24 targets against the wall and we measured them with GPT and DI-
1000, which are linked together for the on-line measurement and relative orientation. For stereo-photographing we
used Nikon digital camera D1 set at 8m from the object. And the data was brought back to the office and processed
through stereo-matching measurement of PI-2000 and the three-dimensional data was again fed back to DI-1000 to
produce the final image.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B5. Amsterdam 2000. 437
