Abstract
Estimation of surface curvature from range data is important for a range of tasks in computer vision and robotics, object segmentation, object recognition and robotic grasping estimation. This work presents a fast method of robustly computing accurate metric principal curvature values from noisy point clouds which was implemented on GPU. In contrast to existing readily available solutions which first differentiate the surface to estimate surface normals and then differentiate these to obtain curvature, amplifying noise, our method iteratively fits parabolic quadric surface patches to the data. Additionally previous methods with a similar formulation use less robust techniques less applicable to a high noise sensor. We demonstrate that our method is fast and provides better curvature estimates than existing techniques. In particular we compare our method to several alternatives to demonstrate the improvement.
Original language | English |
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Title of host publication | Australasian Conference on Robotics and Automation, ACRA 2015 |
Subtitle of host publication | 2-4 December 2015; Canberra, Australia |
Editors | Robert Mahony, Jonghyuk Kim, Hongdong Li |
Place of Publication | Canberra ACT Australia |
Publisher | Australian Robotics and Automation Association (ARAA) |
Pages | 33-41 |
Number of pages | 9 |
ISBN (Electronic) | 9780980740462 |
ISBN (Print) | 9781510819269 |
Publication status | Published - 2015 |
Event | Australasian Conference on Robotics and Automation 2015 - Australian National University (ANU), Canberra, Australia Duration: 2 Dec 2015 → 4 Dec 2015 http://www.araa.asn.au/conferences/acra-2015/ |
Conference
Conference | Australasian Conference on Robotics and Automation 2015 |
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Abbreviated title | ACRA 2015 |
Country | Australia |
City | Canberra |
Period | 2/12/15 → 4/12/15 |
Internet address |