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Sensors 2012, 12(12), 17186-17207; doi:10.3390/s121217186
Article

Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

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Received: 8 October 2012; in revised form: 7 December 2012 / Accepted: 11 December 2012 / Published: 12 December 2012
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Abstract: Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.
Keywords: terrain reconstruction; 3D ground segmentation; 3D boundary estimation; height histogram; Gibbs-Markov Random Field terrain reconstruction; 3D ground segmentation; 3D boundary estimation; height histogram; Gibbs-Markov Random Field
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Song, W.; Cho, K.; Um, K.; Won, C.S.; Sim, S. Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation. Sensors 2012, 12, 17186-17207.

AMA Style

Song W, Cho K, Um K, Won CS, Sim S. Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation. Sensors. 2012; 12(12):17186-17207.

Chicago/Turabian Style

Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee S.; Sim, Sungdae. 2012. "Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation." Sensors 12, no. 12: 17186-17207.


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