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Fast Gaussian process occupancy maps (GPOM) for dynamic environments using Big Data GP
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README.md

Learning highly dynamic environments with stochastic variational inference

Variational Sparse Dynamic Gaussian Process Occupancy Maps (VSDGPOM)

Fast Gaussian process occupancy maps (GPOM) for dynamic environments using Big Data GP

Required software: python 2.7 (last tested), numpy, matplotlib, sklearn, and GPflow 0.3.5 (last tested)

Demonstration The demo input file has 173234 data points. A conventional Gaussian process is limited to a few thousand data points.

Paper:

@inproceedings{senanayake2017learning,
  title={Learning highly dynamic environments with stochastic variational inference},
  author={Senanayake, Ransalu and O'Callaghan, Simon and Ramos, Fabio},
  booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
  pages={2532--2539},
  year={2017},
  organization={IEEE}
}

Video: https://youtu.be/RItH8HH82ss

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