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Path Clustering with Homology Area
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-8750-0897
CUNY College of Staten Island, Mathematics Department, New York, USA.ORCID iD: 0000-0001-6322-7542
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-1114-6040
2018 (English)In: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2018, p. 7346-7353Conference paper, Published paper (Refereed)
Abstract [en]

Path clustering has found many applications in recent years. Common approaches to this problem use aggregates of the distances between points to provide a measure of dissimilarity between paths which do not satisfy the triangle inequality. Furthermore, they do not take into account the topology of the space where the paths are embedded. To tackle this, we extend previous work in path clustering with relative homology, by employing minimum homology area as a measure of distance between homologous paths in a triangulated mesh. Further, we show that the resulting distance satisfies the triangle inequality, and how we can exploit the properties of homology to reduce the amount of pairwise distance calculations necessary to cluster a set of paths. We further compare the output of our algorithm with that of DTW on a toy dataset of paths, as well as on a dataset of real-world paths.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2018. p. 7346-7353
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-247259DOI: 10.1109/ICRA.2018.8460939Scopus ID: 2-s2.0-85063140347OAI: oai:DiVA.org:kth-247259DiVA, id: diva2:1297732
Conference
2018 IEEE International Conference on Robotics and Automation (ICRA)
Note

QC 20190513

Available from: 2019-03-20 Created: 2019-03-20 Last updated: 2019-05-13Bibliographically approved

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Carvalho, Joao FredericoVejdemo-Johansson, MikaelKragic, DanicaPokorny, Florian T.

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Robotics, perception and learning, RPLCentre for Autonomous Systems, CAS
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