Uncertainty-aware occupancy map prediction using generative networks for robot navigation

Kapil Katyal, Katie Popek, Chris Paxton, Phil Burlina, Gregory D. Hager

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Efficient exploration through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future motion is often severely limited by sensor field of view (FOV). By contrast, biological systems routinely make decisions by taking into consideration what might exist beyond their FOV based on prior experience. We present an approach for predicting occupancy map representations of sensor data for future robot motions using deep neural networks. We develop a custom loss function used to make accurate prediction while emphasizing physical boundaries. We further study extensions to our neural network architecture to account for uncertainty and ambiguity inherent in mapping and exploration. Finally, we demonstrate a combined map prediction and information-theoretic exploration strategy using the variance of the generated hypotheses as the heuristic for efficient exploration of unknown environments.

Original languageEnglish (US)
Title of host publication2019 International Conference on Robotics and Automation, ICRA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5453-5459
Number of pages7
ISBN (Electronic)9781538660263
DOIs
StatePublished - May 2019
Event2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada
Duration: May 20 2019May 24 2019

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2019-May
ISSN (Print)1050-4729

Conference

Conference2019 International Conference on Robotics and Automation, ICRA 2019
CountryCanada
CityMontreal
Period5/20/195/24/19

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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  • Cite this

    Katyal, K., Popek, K., Paxton, C., Burlina, P., & Hager, G. D. (2019). Uncertainty-aware occupancy map prediction using generative networks for robot navigation. In 2019 International Conference on Robotics and Automation, ICRA 2019 (pp. 5453-5459). [8793500] (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2019.8793500