A full data set for a given mission may
include tens to hundreds of terabytes. All that data must be processed to provide
three-dimensional point-cloud data
sets, which the customer can “observe”
using any of several Geospatial Information System (GIS) software packages,
such as Google Earth.
Supercomputers used for processing
these massive data sets consist of large
numbers of processors networked into
clusters, or cloud-based computing systems.
A cluster consists of many processors
networked in a proprietary system.
Supervisory software breaks the computing
task up, and assigns different
parts of the job to different processors,
which operate individually in a series/
parallel mode to process it in the most
In series (pipeline) mode, the processors
operate sequentially on the same
part of the data set. Each processor performs
a different task to partially
process the data. Subsequent processors
further process the data until the programmed
operations result in the finished
In parallel mode, different processors
perform the same operations on different
parts of the data set. The end result
is, again, a fully processed data set in significantly
shorter time than possible
with single-processor systems.
Cloud computing is similar to cluster
computing, except that the problem is
run not on a defined set of processors
owned by the user, but by “renting” slices
of computer time on Internet-based
servers. Cloud computing is ideal for
customers whose usage levels or other
considerations (such as security issues)
cannot justify the investment in proprietary
The final result of the processing is
typically a 3D point cloud, a 3D mesh
model, and standard orthographic 2D
image datasets. A point cloud is a dense
array of points defining a three-dimensional
surface. Each point has attribute
data, such as x, y, and z coordinates,
along with surface-optical information,
such as color, transparency, and
reflectance. Taken together, the point
cloud forms a 3D map of the surface.
Users employ GIS software to view the
point cloud. One can view it as a static
map, a display from different viewpoints,
or even “fly” over it as an animated view.
The fully processed data set can be delivered
on a CD, over the Internet, or via a
URL where the user can browse the full
data set. Significantly, the user can view
the data set as a complete unit — a complete
map, as it were — rather than in
bits and pieces.
What the customer ultimately buys is
the data collection system — the camera
arrays and airborne data servers —
and a mapping service. The user then
flies missions with the data collection
system, and delivers the raw image data to UR. The company processes the data
and returns the completed map and 3D
point cloud within twenty-four hours.
The customer can then view the map
via third-party GIS software, such as
ArcView (Esri) [www.esri.com/software/arcview], or FalconView, [www.falconview.org/trac/FalconView], which
is a PC-based mapping application
developed by the Georgia Tech
Research Institute for the U.S.
Department of Defense. Or the customer
can obtain PeARL software from
UR to run on their own supercomputer
cluster or cloud.
The alternative to PeARL is combining
lower-resolution video systems with
light direction and ranging (LIDAR)
systems to capture both lateral and vertical
information. LIDAR systems operate
similar to RADAR, but use light pulses
instead of microwave pulses. The
LIDAR system sends out a series of laser
pulses to illuminate targets on the
ground. The system then measures the
time each pulse takes to reach the
ground and return. That time multiplied
by the speed of light equals the
total travel-path length, which is twice
the range to the target.
LIDAR systems are quite accurate, but
necessarily sample relatively few points
on the ground, leading to a low-resolution
map. In addition, processing of the
collected data can take several days to
A number of agencies including the
Department of Homeland Security and
U.S. military services have found the
PeARL system to be an ideal solution.
They have applied the system to disaster
response (such as storms and natural
disasters), emergency response (such as
industrial accidents), and battlefield
reconnaissance missions. The characteristics
of these applications include the
need for up-to-date high-resolution
maps of extended areas; the need for 3D
information; recent events that render
traditional topographic maps obsolete;
and the need for quick turnaround.
This article was written by Geoff Peters,
CEO, Urban Robotics (Portland, OR). For
more information, contact Mr. Peters at
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