SemanticSLAM: Using Environment Landmarks for Unsupervised Indoor Localization

@article{Abdelnasser2016SemanticSLAMUE,
  title={SemanticSLAM: Using Environment Landmarks for Unsupervised Indoor Localization},
  author={Heba Abdelnasser and Reham Mohamed and Ahmed Elgohary and Moustafa Farid Alzantot and Junyu Wang and Souvik Sen and Romit Roy Choudhury and Moustafa Youssef},
  journal={IEEE Transactions on Mobile Computing},
  year={2016},
  volume={15},
  pages={1770-1782}
}
Indoor localization using mobile sensors has gained momentum lately. Most of the current systems rely on an extensive calibration step to achieve high accuracy. We propose SemanticSLAM, a novel unsupervised indoor localization scheme that bypasses the need for war-driving. SemanticSLAM leverages the idea that certain locations in an indoor environment have a unique signature on one or more phone sensors. Climbing stairs, for example, has a distinct pattern on the phone's accelerometer; a… CONTINUE READING
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SemSense: Automatic construction of semantic indoor floorplans

2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN) • 2015
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