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Rough terrain profiling using digital image correlation
Institution:1. U.S. Army Tank Automotive Research Development and Engineering Center, Warren, MI 48397-5000, USA;2. Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1572, USA;1. Division of System Research, Faculty of Engineering, Yokohama National University, Tokiwadai 79-5, Hodogaya-ku, Yokohama 240-8501, Japan;2. Exponent Inc., Natick, MA 01760, USA;3. Laboratory for Manufacturing and Productivity, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;1. Center for Advanced Vehicular Systems (CAVS), Mississippi State University, Mississippi State, MS 39762, USA;2. Dept. of Civil and Environmental Engineering and Center for Advanced Vehicular Systems (CAVS), Mississippi State University, Mississippi State, MS 39762, USA;3. Terracon Consultants, Inc., Chattanooga, TN 37406, USA;4. U.S. Army Engineer Research and Development Center (ERDC), 3909 Halls Ferry Road, Vicksburg, MS 39180, USA
Abstract:Road profiling is an important aspect of vehicle dynamics simulations especially over rough terrains. The accurate measurement of rough terrains allows for more accurate multi body simulations. Three dimensional road profiles are usually performed by utilising a line scan sensor which measures several points lateral to the road. The sensors range from simple road following wheels to LiDAR sensors. The obtained line scans are longitudinally stitched together using the orientation and position of the sensor to obtain a full three dimensional road profile. The sensor’s position and orientation therefore needs to be accurately determined in order to combine the line scans to create an accurate representation of the terrain. The sensor’s position and orientation is normally measured using an expensive inertial measurement unit or Inertial Navigation System (INS) with high sensitivity, low noise and low drift. This paper proposes a road profiling technique which utilises stereography, based on two inexpensive digital cameras, to obtain three-dimensional measurements of the road. The system negates the use of an expensive INS system to determine orientation and position. The data sets also require subsampling which can be computationally expensive. A simple subsampling routine is presented which takes advantage of the structure of the data sets to significantly speed up the process.
Keywords:Rough  Road profiling  Profilometer  Digital image correlation  Point cloud interpolation  Camera stereovision
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