<div class="csl-bib-body">
<div class="csl-entry">Langer, E., Patten, T. M., & Vincze, M. (2022). Where Does It Belong? Autonomous Object Mapping in Open-World Settings. <i>Frontiers in Robotics and AI</i>, <i>9</i>, Article 828732. https://doi.org/10.3389/frobt.2022.828732</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/177601
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dc.description.abstract
Detecting changes such as moved, removed, or new objects is the essence for numerous indoor applications in robotics such as tidying-up, patrolling, and fetch/carry tasks. The problem is particularly challenging in open-world scenarios where novel objects may appear at any time. The main idea of this paper is to detect objects from partial 3D reconstructions of interesting areas in the environment. In our pipeline we first identify planes, consider clusters on top as objects, and compute their point-pair-features. They are used to match potential objects and categorize them robustly into static, moved, removed, and novel objects even in the presence of partial object reconstructions and clutter. Our approach dissolves heaps of objects without specific object knowledge, but only with the knowledge acquired from change detection. The evaluation is performed on real-world data that includes challenges affecting the quality of the reconstruction as a result of noisy input data. We present the novel dataset ObChange for quantitative evaluation, and we compare our method against a baseline using learning-based object detection. The results show that, even with a targeted training set, our approach outperforms the baseline for most test cases. Lastly, we also demonstrate our method's effectiveness in real robot experiments.
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dc.description.sponsorship
FWF Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
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dc.description.sponsorship
FWF Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
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dc.description.sponsorship
FFG - Österr. Forschungsförderungs- gesellschaft mbH
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dc.language.iso
en
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dc.publisher
Frontiers
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dc.relation.ispartof
Frontiers in Robotics and AI
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
autonomous robot
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dc.subject
object detection
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dc.subject
object mapping
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dc.subject
object matching
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dc.subject
open-world detection
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dc.subject
point-pair-features
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dc.title
Where Does It Belong? Autonomous Object Mapping in Open-World Settings
Extrahieren von Daten aus Hand Manipulation für die in-Hand Manipulation von Objekten am Roboter um die Fingerfertigkeit und autonomie von Robotern zu erhöhen
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tuw.project.title
Benchmark zum Verstehen des Greifens von Objekten
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tuw.project.title
Künstliche Intelligenz für den Handel - Erkennnug einzelner Objekte in Stapeln