Continuous representations of objects have always been used in robotics in the form of geometric primitives and surface models. Recently, learning techniques have emerged which allow more complex continuous representations to be learned from data, but these learning techniques require training data in the form of watertight meshes which restricts their application as meshes of this form are difficult to obtain from real data. This paper proposes a modification to existing methods that allows real world point cloud data to be used for training these surface representations allowing the techniques to be used in broader applications. The modification is evaluated on ModelNet10 to quantify the difference between the existing and the proposed methods as well as on a novel precision agriculture dataset that has been released publicly to show the modification's applicability to new areas. The proposed method enables obtaining training data from real world sensors that produce point clouds rather than requiring an expensive meshing step which may not be possible for some applications. This opens the possibility of using techniques like this for complex shapes in areas like grasping and agricultural data collection.
|Original language||English (US)|
|Title of host publication||2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Oct 24 2020|
|Event||2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States|
Duration: Oct 24 2020 → Jan 24 2021
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Conference||2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020|
|Period||10/24/20 → 1/24/21|
Bibliographical noteFunding Information:
The authors would like to thank all the members of the Center for Distributed Robotics Laboratory for their help. This material is based upon work partially supported by the Corn Growers Association of MN, the Minnesota Robotics Institute (MnRI), Honeywell, and the National Science Foundation through grants #CNS-1439728, #CNS-1531330, and #CNS-1939033. USDA/NIFA has also supported this work through the grant 2020-67021-30755.
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