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Sensors 2012, 12(8), 11221-11237; doi:10.3390/s120811221
Article

Complete Scene Recovery and Terrain Classification in Textured Terrain Meshes

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Received: 16 July 2012; in revised form: 7 August 2012 / Accepted: 7 August 2012 / Published: 13 August 2012
(This article belongs to the Special Issue Ubiquitous Sensing)
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Abstract: Terrain classification allows a mobile robot to create an annotated map of its local environment from the three-dimensional (3D) and two-dimensional (2D) datasets collected by its array of sensors, including a GPS receiver, gyroscope, video camera, and range sensor. However, parts of objects that are outside the measurement range of the range sensor will not be detected. To overcome this problem, this paper describes an edge estimation method for complete scene recovery and complete terrain reconstruction. Here, the Gibbs-Markov random field is used to segment the ground from 2D videos and 3D point clouds. Further, a masking method is proposed to classify buildings and trees in a terrain mesh.
Keywords: mobile robot; terrain reconstruction; multisensor integration; Gibbs-MRF; classification mobile robot; terrain reconstruction; multisensor integration; Gibbs-MRF; classification
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. Complete Scene Recovery and Terrain Classification in Textured Terrain Meshes. Sensors 2012, 12, 11221-11237.

AMA Style

Song W, Cho K, Um K, Won CS, Sim S. Complete Scene Recovery and Terrain Classification in Textured Terrain Meshes. Sensors. 2012; 12(8):11221-11237.

Chicago/Turabian Style

Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae. 2012. "Complete Scene Recovery and Terrain Classification in Textured Terrain Meshes." Sensors 12, no. 8: 11221-11237.



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