Complete scene recovery and terrain classification in textured terrain meshes

Wei Song, Kyungeun Cho, Kyhyun Um, Chee Sun Won, Sungdae Sim

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Pages (from-to)11221-11237
Number of pages17
JournalSensors
Volume12
Issue number8
DOIs
StatePublished - Aug 2012

Keywords

  • Classification
  • Gibbs-MRF
  • Mobile robot
  • Multisensor integration
  • Terrain reconstruction

Fingerprint

Dive into the research topics of 'Complete scene recovery and terrain classification in textured terrain meshes'. Together they form a unique fingerprint.

Cite this