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27 April 2018 Scanning LiDAR for airfield damage assessment
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The ability to rapidly assess damage to military infrastructure after an attack is the object of ongoing research. In the case of runways, sensor systems capable of detecting and locating craters, spall, unexploded ordinance, and debris are necessary to quickly and efficiently deploy assets to restore a minimum airfield operating surface. We describe measurements performed using two commercial, robotic scanning LiDAR systems during a round of testing at an airfield. The LiDARs were used to acquire baseline data and to conduct scans after two rounds of demolition and placement of artifacts for the entire runway. Configuration of the LiDAR systems was sub-optimal due to availability of only two platforms for placement of sensors on the same side of the runway. Nevertheless, results prove that the spatial resolution, accuracy, and cadence of the sensors is sufficient to develop point cloud representations of the runway sufficient to distinguish craters, debris and most UXO. Location of a complementary set of sensors on the opposite side of the runway would alleviate the observed shadowing, increase the density of the registered point cloud, and likely allow detection of smaller artifacts. Importantly, the synoptic data acquired by these static LiDAR sensors is dense enough to allow registration (fusion) with the smaller, denser, targeted point cloud data acquired at close range by unmanned aerial systems. The paper will also discuss point cloud manipulation and 3D object recognition algorithms that the team is developing for automatic detection and geolocation of damage and objects of interest.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian Robinson, Woody English, Darrell Langford, Josh Walters, Bill Angotta, Stuart Fowler, and Robert Diltz "Scanning LiDAR for airfield damage assessment", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106461A (27 April 2018);


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