Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Meaningful maps with object-oriented semantic mapping|
|Citation:||IEEE, 2017 / vol.2017-September, pp.5079-5085|
|Series/Report no.:||IEEE International Conference on Intelligent Robots and Systems|
|Conference Name:||2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (24 Sep 2017 - 28 Sep 2017 : Vancouver, Canada)|
|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.|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.