Goals

  1. To explore neural networks training using various data types (images from camera, sensor measurements, maps, etc.) and to analyze methods of obtaining selected data in indoor environment.
  2. To propose and to implement a neural network bound to a specific building with focus on its applicability in particular indoor navigation and positioning problems.
  3. To evaluate the performance of trained neural network in particular problems and the practical applicability in indoor navigation application.

Literature

  1. Mendoza-Silva, G.M., Torres-Sospedra, J. and Huerta, J., 2019. A meta-review of indoor positioning systems. Sensors, 19(20), p.4507.
  2. Noroozi, M. and Favaro, P., 2016, October. Unsupervised learning of visual representations by solving jigsaw puzzles. In European conference on computer vision (pp. 69-84). Springer, Cham.
  3. Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N. and Huang, X., 2020. Pre-trained models for natural language processing: A survey. Science China Technological Sciences, pp.1-26.