An efficiently computable metric for comparing polygonal shapes
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
Journal of Algorithms
Map-matching for low-sampling-rate GPS trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
An Offline Map Matching via Integer Programming
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Go with the flow: the direction-based fréchet distance of polygonal curves
TAPAS'11 Proceedings of the First international ICST conference on Theory and practice of algorithms in (computer) systems
Map matching with inverse reinforcement learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We propose a novel scheme for map matching and fully autonomous self-localization. Our scheme is based on the unique characteristics of the shape of paths in a road network. As uniqueness of path shapes comes as no surprise in a world of infinite precision, we develop robust means of comparing shapes of paths under imprecisions. Even under this fuzzy comparison model, path shapes turn out to be sufficiently characteristic to allow for map matching or fully autonomous self-localization. We design an efficient data structure which allows for very fast path shape queries.