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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.


Height estimation process.
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f7-sensors-12-11221: Height estimation process.

Mentions: We find the boundary's y coordinates using an inverse process of projection from 2D pixels to 3D points. We place the camera centre at the origin. The projection ray from the origin to the object vertex gives an estimate of the height of that object vertex, as shown in Figure 7. Because the horizon coordinates of the 3D object vertex in the terrain mesh are fixed, we update the elevation value of each object vertex in the terrain mesh to obtain the results shown in Figure 8.


Complete scene recovery and terrain classification in textured terrain meshes.

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

Height estimation process.
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-12-11221: Height estimation process.
Mentions: We find the boundary's y coordinates using an inverse process of projection from 2D pixels to 3D points. We place the camera centre at the origin. The projection ray from the origin to the object vertex gives an estimate of the height of that object vertex, as shown in Figure 7. Because the horizon coordinates of the 3D object vertex in the terrain mesh are fixed, we update the elevation value of each object vertex in the terrain mesh to obtain the results shown in Figure 8.

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.