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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.


Boundary searching in 2D image.
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f6-sensors-12-11221: Boundary searching in 2D image.

Mentions: Using the ground data segmentation result, we assume that the non-ground vertices in the terrain mesh belong to objects, because background data, such as the sky, cannot be sensed by the range sensor. Next, we project these vertices onto pixels in a 2D image, whose configuration is determined as being part of an object. We apply the Gibbs-MRF method to classify the non-ground pixels into objects and background classes, in order to detect the boundary pixels between objects and background. The boundary detection results are shown as red pixels in Figure 6.


Complete scene recovery and terrain classification in textured terrain meshes.

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

Boundary searching in 2D image.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-12-11221: Boundary searching in 2D image.
Mentions: Using the ground data segmentation result, we assume that the non-ground vertices in the terrain mesh belong to objects, because background data, such as the sky, cannot be sensed by the range sensor. Next, we project these vertices onto pixels in a 2D image, whose configuration is determined as being part of an object. We apply the Gibbs-MRF method to classify the non-ground pixels into objects and background classes, in order to detect the boundary pixels between objects and background. The boundary detection results are shown as red pixels in Figure 6.

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.