Learning continuous object representations from point cloud data

Henry J. Nelson, Nikolaos Papanikolopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 languageEnglish (US)
Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2446-2451
Number of pages6
ISBN (Electronic)9781728162126
DOIs
StatePublished - Oct 24 2020
Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States
Duration: Oct 24 2020Jan 24 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
CountryUnited States
CityLas Vegas
Period10/24/201/24/21

Bibliographical note

Funding 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.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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