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Complete scene recovery and terrain classification in textured terrain meshes.

Song W, Cho K, Um K, Won CS, Sim S - Sensors (Basel) (2012)

Bottom Line: 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.Here, the Gibbs-Markov random field is used to segment the ground from 2D videos and 3D point clouds.

View Article: PubMed Central - PubMed

Affiliation: Department of Multimedia Engineering, Dongguk University-Seoul, 26 Pildosng 3 Ga, Jung-gu, Seoul 100-715, Korea. songwei@dongguk.edu

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.

No MeSH data available.


Segmentation and classification results. (a) Complete scene recovery. (b) Ground segmentation in the terrain mesh. (c) Tree classification in the terrain mesh. (d) Building classification in the terrain mesh.
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f11-sensors-12-11221: Segmentation and classification results. (a) Complete scene recovery. (b) Ground segmentation in the terrain mesh. (c) Tree classification in the terrain mesh. (d) Building classification in the terrain mesh.

Mentions: The pixels with a ground configuration are grouped into dataset , which is shown as the blue region. The other regions contain objects and background textures. We classify the ground vertices in the 3D terrain mesh, which are mapped to the pixels in the dataset , as shown in Figure 11(b).


Complete scene recovery and terrain classification in textured terrain meshes.

Song W, Cho K, Um K, Won CS, Sim S - Sensors (Basel) (2012)

Segmentation and classification results. (a) Complete scene recovery. (b) Ground segmentation in the terrain mesh. (c) Tree classification in the terrain mesh. (d) Building classification in the terrain mesh.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472881&req=5

f11-sensors-12-11221: Segmentation and classification results. (a) Complete scene recovery. (b) Ground segmentation in the terrain mesh. (c) Tree classification in the terrain mesh. (d) Building classification in the terrain mesh.
Mentions: The pixels with a ground configuration are grouped into dataset , which is shown as the blue region. The other regions contain objects and background textures. We classify the ground vertices in the 3D terrain mesh, which are mapped to the pixels in the dataset , as shown in Figure 11(b).

Bottom Line: 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.Here, the Gibbs-Markov random field is used to segment the ground from 2D videos and 3D point clouds.

View Article: PubMed Central - PubMed

Affiliation: Department of Multimedia Engineering, Dongguk University-Seoul, 26 Pildosng 3 Ga, Jung-gu, Seoul 100-715, Korea. songwei@dongguk.edu

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.

No MeSH data available.