Fast plane extraction in organized point clouds using agglomerative hierarchical clustering

Chen Feng, Yuichi Taguchi, Vineet R. Kamat

Research output: Contribution to journalConference articlepeer-review


Real-Time plane extraction in 3D point clouds is crucial to many robotics applications. We present a novel algorithm for reliably detecting multiple planes in real time in organized point clouds obtained from devices such as Kinect sensors. By uniformly dividing such a point cloud into non-overlapping groups of points in the image space, we first construct a graph whose node and edge represent a group of points and their neighborhood respectively. We then perform an agglomerative hierarchical clustering on this graph to systematically merge nodes belonging to the same plane until the plane fitting mean squared error exceeds a threshold. Finally we refine the extracted planes using pixel-wise region growing. Our experiments demonstrate that the proposed algorithm can reliably detect all major planes in the scene at a frame rate of more than 35Hz for 640×480 point clouds, which to the best of our knowledge is much faster than state-of-The-Art algorithms.

Original languageEnglish (US)
Article number6907776
Pages (from-to)6218-6225
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - Sep 22 2014
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


Dive into the research topics of 'Fast plane extraction in organized point clouds using agglomerative hierarchical clustering'. Together they form a unique fingerprint.

Cite this