Hilbert maps: Scalable continuous occupancy mapping with stochastic gradient descent

@article{Ramos2015HilbertMS,
  title={Hilbert maps: Scalable continuous occupancy mapping with stochastic gradient descent},
  author={Fabio Tozeto Ramos and Lionel Ott},
  journal={The International Journal of Robotics Research},
  year={2015},
  volume={35},
  pages={1717 - 1730}
}
  • F. Ramos, Lionel Ott
  • Published 13 July 2015
  • Computer Science
  • The International Journal of Robotics Research
The vast amount of data robots can capture today motivates the development of fast and scalable statistical tools to model the space the robot operates in. We devise a new technique for environment representation through continuous occupancy mapping that improves on the popular occupancy grip maps in two fundamental aspects: (1) it does not assume an a priori discrimination of the world into grid cells and therefore can provide maps at an arbitrary resolution; (2) it captures spatial… 

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