Inside Unmanned Systems

OCT-NOV 2016

Inside Unmanned Systems provides actionable business intelligence to decision-makers and influencers operating within the global UAS community. Features include analysis of key technologies, policy/regulatory developments and new product design.

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32 unmanned systems inside   October/November 2016 LAND AUTOMATED MACHINE GUIDANCE BY THE NUMBERS $ 250,000 The extra cost incurred of mistakenly adding 1/4 inch of extra material over 10 miles of road. Source: Ron Singh, engineering automation manager, Oregon Department of Transportation "IF YOU DON'T UNDERSTAND how this technology works, you leave yourself open to manufacturers and contractors taking advantage of you." Paul McDaniel, owner, Advanced Geodetic Applications ODOT. "Aibotix is working on adapting a LiDAR sensor for this particular hexacopter." The drone navigates using GPS RTK (Real Time Kinematic) technology, and is designed to be controlled by a robotic total station. Singh and his colleagues are using the drone to generate 3-D maps from orthorectified imag- ery that accounts for topographical variations in the surface of the Earth and the tilt of the drone. Specifically, they produce digital ter- rain models via structure-from-motion tech- nologies that estimate 3-D structures from multiple, overlapping 2-D images. "From my perspective, a drone is like a tri- pod for surveying, only you can place it 200 feet in the air over a particular spot," Singh said. "It's a less expensive and quicker way of moving a sensor to the right position com- pared to a boom truck or a fixed-wing f light, and in many ways safer—we don't have to have crews rappel down bridges, but instead just f ly a drone into a position where it can capture some data." Learning Curve Singh and his team are used to producing dig- ital terrain models from high-altitude aerial imagery, but "we are finding anomalies that come from the use of a drone being so close to the ground, and we are learning to change our methodologies to correct for that," he said. "Some of the anomalies come because we're using structure-from-motion technologies," Singh said. "For example, let's say we're pro- ducing a digital terrain model purely from imagery. If we have things like a vertical wall, and the imagery is coming primarily from a camera pointed straight down in the nadir po- sition, then we find that the wall looks wavy. However, if we include some oblique images— if, for part of the f light mission, we turn the camera so it f lies along that wall to produce more images of it—then we create a surface model of that wall that is much more correct, that is crisp and sharp." Another set of anomalies comes from tall grass and bushes. A crew monitors the progress of an asphalt road paver. Photo courtesy of Ron Singh

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