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Real-Time Plane Segmentation and Obstacle Detection of 3D Point Clouds for Indoor Scenes

  • Zhe Wang
  • Hong Liu
  • Yueliang Qian
  • Tao Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7584)

Abstract

Scene analysis is an important issue in computer vision and extracting structural information is one of the fundamental techniques. Taking advantage of depth camera, we propose a novel fast plane segmentation algorithm and use it to detect obstacles in indoor environment. The proposed algorithm has two steps: the initial segmentation and the refined segmentation. Firstly, depth image is converted into 3D point cloud and divided into voxels, which are less sensitive to noises compared with pixels. Then area-growing algorithm is used to extract the candidate planes according to the normal of each voxel. Secondly, each point that hasn’t been classified to any plane is examined whether it actually belongs to a plane. The two-step strategy has been proven to be a fast segmentation method with high accuracy. The experimental results demonstrate that our method can segment planes and detect obstacles in real-time with high accuracy for indoor scenes.

Keywords

plane segmentation point cloud obstacle detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhe Wang
    • 1
  • Hong Liu
    • 1
  • Yueliang Qian
    • 1
  • Tao Xu
    • 1
  1. 1.Key Laboratory of Intelligent Information Processing &&, Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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