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Publications:Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer
Abstract <p>We propose a method based on a no<p>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%). © 2018 IEEE.</p>95.97% vs. 81.56%). © 2018 IEEE.</p>
Author Saeed Gholami Shahbandi + , Martin Magnusson + , Karl Iagnemma +
Conference 2018 IEEE International Conference on Robotics and Automation, Brisbane, Australia, May 21-25, 2018
DOI http://dx.doi.org/10.1109/LRA.2018.2806439  +
Diva http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1197163
EndPage 2047  +
Issue 3  +
Journal IEEE Robotics and Automation Letters  +
PublicationType Journal Paper  +
Publisher IEEE  +
StartPage 2040  +
Title Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer  +
Volume 3  +
Year 2018  +
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Categories Publication  +
Modification dateThis property is a special property in this wiki. 12 April 2018 20:22:46  +
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