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Tag: Remote assessment
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  • Enhanced Route Reconnaissance—Generation 1

    Abstract: The movement of soldiers and materiel across battlespace is critical to a successful military operation. Knowledge of the road network condition ensures safe and successful vehicle maneuver. This research focused on remote assessment of poor-quality paved road networks for vehicle maneuver using data products derived from three-dimensional point clouds. Point clouds were generated from lidar sensors deployed from ground and airborne platforms to enable engineering analysis of the pavement surface. A series of algorithms developed to extract roughness, grade, radius of curvature, and width along the road network ensured storage of information for graphical display. A vehicle speed lookup table was calculated by conducting computer simulations using the NATO Reference Mobility Model over a range of road parameters. The lookup table enabled determination of the maximum allowable speed for a given vehicle type associated with the extracted road parameters. A graphical interface, developed for displaying the percentage speed reduction as either red, amber, or green squares along the road network, provided visual assessments of road condition. This report summarizes developing a software suite to calculate and visualize speed reduction over a road network as a function of route geometry, condition, and vehicle type. The interface developed can aid in critical logistical decisions that influence the success of military maneuver operations.
  • Methodology for Remote Assessment of Pavement Distresses from Point Cloud Analysis

    Abstract: The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.