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Topological Trajectory Clustering with Relative Persistent Homology
2016 (English)In: 2016 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 16-23Conference paper, Published paper (Refereed)
Abstract [en]

Cloud Robotics techniques based on Learning from Demonstrations suggest promising alternatives to manual programming of robots and autonomous vehicles. One challenge is that demonstrated trajectories may vary dramatically: it can be very difficult, if not impossible, for a system to learn control policies unless the trajectories are clustered into meaningful consistent subsets. Metric clustering methods, based on a distance measure, require quadratic time to compute a pairwise distance matrix and do not naturally distinguish topologically distinct trajectories. This paper presents an algorithm for topological clustering based on relative persistent homology, which, for a fixed underlying simplicial representation and discretization of trajectories, requires only linear time in the number of trajectories. The algorithm incorporates global constraints formalized in terms of the topology of sublevel or superlevel sets of a function and can be extended to incorporate probabilistic motion models. In experiments with real automobile and ship GPS trajectories as well as pedestrian trajectories extracted from video, the algorithm clusters trajectories into meaningful consistent subsets and, as we show in an experiment with ship trajectories, results in a faster and more efficient clustering than a metric clustering by Frechet distance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 16-23
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-199783ISI: 000389516200003Scopus ID: 2-s2.0-84977532330ISBN: 978-1-4673-8026-3 (print)OAI: oai:DiVA.org:kth-199783DiVA, id: diva2:1066972
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 16-21, 2016, Royal Inst Technol, Ctr Autonomous Syst, Stockholm, SWEDEN
Note

QC 20170119

Available from: 2017-01-19 Created: 2017-01-16 Last updated: 2017-01-19Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf