Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111586
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Type: Conference paper
Title: Meaningful maps with object-oriented semantic mapping
Author: Sünderhauf, N.
Pham, T.
Latif, Y.
Milford, M.
Reid, I.
Citation: IEEE, 2017 / vol.2017-September, pp.5079-5085
Publisher: IEEE
Issue Date: 2017
Series/Report no.: IEEE International Conference on Intelligent Robots and Systems
ISBN: 9781538626825
ISSN: 2153-0858
2153-0866
Conference Name: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (24 Sep 2017 - 28 Sep 2017 : Vancouver, Canada)
Statement of
Responsibility: 
Niko Sünderhauf, Trung T. Pham, Yasir Latif, Michael Milford, Ian Reid
Abstract: For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. The majority of research to date has addressed these mapping challenges separately, focusing on either geometric or semantic mapping. In this paper we address the problem of building environmental maps that include both semantically meaningful, object-level entities and point- or mesh-based geometrical representations. We simultaneously build geometric point cloud models of previously unseen instances of known object classes and create a map that contains these object models as central entities. Our system leverages sparse, feature-based RGB-D SLAM, image-based deep-learning object detection and 3D unsupervised segmentation.
Rights: ©2017 IEEE
RMID: 0030083169
DOI: 10.1109/IROS.2017.8206392
Grant ID: http://purl.org/au-research/grants/arc/FT140101229
Appears in Collections:Computer Science publications

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