oru.sePublikationer
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Tell me about dynamics!: Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0002-7399-9359
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0002-0804-8637
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0001-5061-5474
Show others and affiliations
2016 (English)In: Robotics: Science and Systems Conference (RSS 2016), 2016Conference paper, Published paper (Refereed)
Resource type
Text
Abstract [en]

Autonomous mobile robots often require informa-tion about the environment beyond merely the shape of thework-space. In this work we present a probabilistic method formappingdynamics, in the sense of learning and representingstatistics about the flow of discrete objects (e.g., vehicles, people)as well as continuous media (e.g., air flow). We also demonstratethe capabilities of the proposed method with two use cases. Onerelates to motion planning in populated environments, whereinformation about the flow of people can help robots to followsocial norms and to learn implicit traffic rules by observingthe movements of other agents. The second use case relates toMobile Robot Olfaction (MRO), where information about windflow is crucial for most tasks, including e.g. gas detection, gasdistribution mapping and gas source localisation. We representthe underlying velocity field as a set of Semi-Wrapped GaussianMixture Models (SWGMM) representing the learnt local PDF ofvelocities. To estimate the parameters of the PDF we employ aformulation of Expectation Maximisation (EM) algorithm specificfor SWGMM. We also describe a data augmentation methodwhich allows to build a dense dynamic map based on a sparseset of measurements. In case only a small set of observations isavailable we employ a hierarchical sampling method to generatevirtual observations from existing mixtures.

Place, publisher, year, edition, pages
2016.
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-55215OAI: oai:DiVA.org:oru-55215DiVA: diva2:1070733
Conference
Robotics: Science and Systems Conference (RSS 2016), Workshop: Geometry and Beyond - Representations, Physics, and Scene Understanding for Robotics, University of Michigan, Ann Arbor, Ml, USA, June 18-22, 2016
Available from: 2017-02-02 Created: 2017-02-02 Last updated: 2017-10-18Bibliographically approved

Open Access in DiVA

fulltext(3723 kB)122 downloads
File information
File name FULLTEXT01.pdfFile size 3723 kBChecksum SHA-512
a9eed760cb023a34aa504835263b261211db580b938fe589f646055d9283ae0f0e7cff1e0ac1e6f8c26c4124147ac9e4d0998c727b31d47a0f0a3079b5d635be
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Kucner, TomaszMagnusson, MartinSchaffernicht, ErikHernandez Bennetts, VictorLilienthal, Achim
By organisation
School of Science and Technology
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 122 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 178 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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