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Bayesian Optimisation for Safe Navigation Under Localisation Uncertainty

  • Rafael OliveiraEmail author
  • Lionel Ott
  • Vitor Guizilini
  • Fabio Ramos
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it against standard BO methods.

Keywords

Bayesian optimisation Gaussian processes Uncertain inputs Traversability mapping Mobile robotics 

Notes

Acknowledgements

This work was supported by CAPES, Brazil (scholarship BEX 13224/13-1), and by Data61/CSIRO, Australia.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rafael Oliveira
    • 1
    Email author
  • Lionel Ott
    • 1
  • Vitor Guizilini
    • 1
  • Fabio Ramos
    • 1
  1. 1.School of Computer ScienceThe University of SydneySydneyAustralia

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