Publications:Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer

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Title Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer
Author Saeed Gholami Shahbandi and Martin Magnusson and Karl Iagnemma
Year 2018
PublicationType Journal Paper
Journal IEEE Robotics and Automation Letters
HostPublication
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 url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1197163
Abstract 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.