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 efficient way.

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 output needed.

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 supercomputing resources.

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.


Figure 3. Eight-camera PeARL array showing overlapping fields of view to provide parallax sensing of height information. (Urban Robotics)
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 several weeks.

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 This email address is being protected from spambots. You need JavaScript enabled to view it., or visit http://info.hotims.com/34460-201.

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