In this paper, we present an approach to segment 3D point cloud data using ideas from persistent homology theory. The proposed algorithms first generate a simplicial complex representation of the point cloud dataset. Next, we compute the zeroth homology group of the complex which corresponds to the number of connected components. Finally, we extract the clusters of each connected component in the dataset. We show that this technique has several advantages over state of the art methods such as the ability to provide a stable segmentation of point cloud data under noisy or poor sampling conditions and its independence of a fixed distance metric.
|Original language||English (US)|
|Title of host publication||2016 IEEE International Conference on Robotics and Automation, ICRA 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jun 8 2016|
|Event||2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden|
Duration: May 16 2016 → May 21 2016
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Other||2016 IEEE International Conference on Robotics and Automation, ICRA 2016|
|Period||5/16/16 → 5/21/16|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation through grants #IIP-0934327, #IIS-1017344, #IIP-1332133, #IIS-1427014, #IIP-1432957, #OISE-1551059, #CNS-1514626, #CNS-1531330, and #CNS-1544887
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