Particle Filter Networks: End-to-End Probabilistic Localization From Visual Observations

@article{Karkus2018ParticleFN,
  title={Particle Filter Networks: End-to-End Probabilistic Localization From Visual Observations},
  author={P{\'e}ter Karkus and David Hsu and Wee Sun Lee},
  journal={CoRR},
  year={2018},
  volume={abs/1805.08975}
}
Particle filters sequentially approximate posterior distributions by sampling representative points and updating them independently. The idea is applied in various domains, e.g. reasoning with uncertainty in robotics. A remaining challenge is constructing probabilistic models of the system, which can be especially hard for complex sensors, e.g. a camera. We introduce the Particle Filter Networks (PF-nets) that encode both a learned probabilistic system model and the particle filter algorithm in… CONTINUE READING

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