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Autonomous Exploration with Expectation-Maximization

  • Jinkun Wang
  • Brendan EnglotEmail author
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

We consider the problem of autonomous mobile robot exploration in an unknown environment for the purpose of building an accurate feature-based map efficiently. Most literature on this subject is focused on the combination of a variety of utility functions, such as curbing robot pose uncertainty and the entropy of occupancy grid maps. However, the effect of uncertain poses is typically not well incorporated to penalize poor localization, which ultimately leads to an inaccurate map. Instead, we explicitly model unknown landmarks as latent variables, and predict their expected uncertainty, incorporating this into a utility function that is used together with sampling-based motion planning to produce informative and low-uncertainty motion primitives. We propose an iterative expectation-maximization algorithm to perform the planning process driving a robot’s step-by-step exploration of an unknown environment. We analyze the performance in simulated experiments, showing that our algorithm maintains the same coverage speed in exploration as competing algorithms, but effectively improves the quality of the resulting map.

Notes

Acknowledgements

This research has been supported in part by the National Science Foundation, grant number IIS-1551391.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringStevens Institute of TechnologyHobokenUSA

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