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Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer
Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden.ORCID iD: 0000-0003-3498-0783
Örebro University, School of Science and Technology. (AASS MRO lab)ORCID iD: 0000-0001-8658-2985
Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge MA, USA.
2018 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 3, p. 2040-2047Article in journal (Refereed) Published
Abstract [en]

We propose a method based on a nonlinear transformation for nonrigid alignment of maps of different modalities, exemplified with matching partial and deformed two-dimensional maps to layout maps. For two types of indoor environments, over a dataset of 40 maps, we have compared the method to state-of-the-art map matching and nonrigid image registration methods and demonstrate a success rate of 80.41% and a mean point-to-point alignment error of 1.78 m, compared to 31.9% and 10.7 m for the best alternative method. We also propose a fitness measure that can quite reliably detect bad alignments. Finally, we show a use case of transferring prior knowledge (labels/segmentation), demonstrating that map segmentation is more consistent when transferred from an aligned layout map than when operating directly on partial maps (95.97% vs. 81.56%).

Place, publisher, year, edition, pages
IEEE Press, 2018. Vol. 3, no 3, p. 2040-2047
National Category
Robotics
Identifiers
URN: urn:nbn:se:oru:diva-66564DOI: 10.1109/LRA.2018.2806439OAI: oai:DiVA.org:oru-66564DiVA, id: diva2:1197226
Conference
IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018
Available from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-04-12Bibliographically approved

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Magnusson, Martin

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CiteExportLink to record
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  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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