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From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs
Örebro University, School of Science and Technology, Örebro University, Sweden. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0001-7035-5710
Örebro University, School of Science and Technology, Örebro University, Sweden. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0002-6013-4874
Örebro University, School of Science and Technology, Örebro University, Sweden. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0003-0217-9326
2016 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, Vol. 1, no 2, 1148-1155 p.Article in journal (Refereed) Published
Abstract [en]

With the increased availability of GPUs and multicore CPUs, volumetric map representations are an increasingly viable option for robotic applications. A particularly important representation is the truncated signed distance field (TSDF) that is at the core of recent advances in dense 3D mapping. However, there is relatively little literature exploring the characteristics of 3D feature detection in volumetric representations. In this paper we evaluate the performance of features extracted directly from a 3D TSDF representation. We compare the repeatability of Integral invariant features, specifically designed for volumetric images, to the 3D extensions of Harris and Shi & Tomasi corners. We also study the impact of different methods for obtaining gradients for their computation. We motivate our study with an example application for building sparse stable scene graphs, and present an efficient GPU-parallel algorithm to obtain the graphs, made possible by the combination of TSDF and 3D feature points. Our findings show that while the 3D extensions of 2D corner-detection perform as expected, integral invariants have shortcomings when applied to discrete TSDFs. We conclude with a discussion of the cause for these points of failure that sheds light on possible mitigation strategies.

Place, publisher, year, edition, pages
Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2016. Vol. 1, no 2, 1148-1155 p.
Keyword [en]
Mapping, recognition
National Category
Computer Science Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-53369DOI: 10.1109/LRA.2016.2523555ScopusID: 2-s2.0-84992291892OAI: oai:DiVA.org:oru-53369DiVA: diva2:1044256
Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2016-11-03Bibliographically approved

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Canelhas, Daniel R.Stoyanov, TodorLilienthal, Achim J.
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School of Science and Technology, Örebro University, Sweden
Computer ScienceComputer Vision and Robotics (Autonomous Systems)

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