Limits...
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.


Line detector masks.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3472881&req=5

f10-sensors-12-11221: Line detector masks.

Mentions: We consider tree objects, including both grass and trees, to have a porous surface that allows rays from the range finder to pierce through to the inside. This is in contrast to buildings, for which the 3D range finder only detects points on the outer surface. Therefore, the horizon shape of a building has a uniform distribution, whereas that for a tree has a normal distribution. As shown in Figure 9, we can see that the horizon structure of the buildings consists of the line-like components. We classify buildings by detecting these lines using the masks described in Figure 10.


Complete scene recovery and terrain classification in textured terrain meshes.

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

Line detector masks.
© Copyright Policy
Related In: Results  -  Collection

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

f10-sensors-12-11221: Line detector masks.
Mentions: We consider tree objects, including both grass and trees, to have a porous surface that allows rays from the range finder to pierce through to the inside. This is in contrast to buildings, for which the 3D range finder only detects points on the outer surface. Therefore, the horizon shape of a building has a uniform distribution, whereas that for a tree has a normal distribution. As shown in Figure 9, we can see that the horizon structure of the buildings consists of the line-like components. We classify buildings by detecting these lines using the masks described in Figure 10.

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.