TY - GEN
T1 - Fast plane extraction in organized point clouds using agglomerative hierarchical clustering
AU - Feng, Chen
AU - Taguchi, Yuichi
AU - Kamat, Vineet R.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/22
Y1 - 2014/9/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84912549634&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84912549634&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2014.6907776
DO - 10.1109/ICRA.2014.6907776
M3 - Conference contribution
AN - SCOPUS:84912549634
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 6218
EP - 6225
BT - Proceedings - IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Y2 - 31 May 2014 through 7 June 2014
ER -