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Autonomous Exploration with Expectation-Maximization

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Robotics Research

Part of the book series: Springer Proceedings in Advanced Robotics ((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.

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References

  1. Blanco, J.L., Fernandez-Madrigal, J.A., González, J.: A novel measure of uncertainty for mobile robot slam with Rao-Blackwellized particle filters. Int. J. Robot. Res. 27(1), 73–89 (2008)

    Article  Google Scholar 

  2. Bourgault, F., Makarenko, A.A., Williams, S.B., Grocholsky, B., Durrant-Whyte, H.F.: Information based adaptive robotic exploration. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 540–545 (2002)

    Google Scholar 

  3. Carrillo, H., Dames, P., Kumar, V., Castellanos, J.A.: Autonomous robotic exploration using occupancy grid maps and graph slam based on shannon and rényi entropy. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 487–494 (2015)

    Google Scholar 

  4. Carrillo, H., Reid, I., Castellanos, J.A.: On the comparison of uncertainty criteria for active SLAM. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2080–2087 (2012)

    Google Scholar 

  5. Celeux, G., Govaert, G.: A classification EM algorithm for clustering and two stochastic versions. Comput. Stat. Data Anal. 14(3), 315–332 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  6. Charrow, B., Liu, S., Kumar, V., Michael, N.: Information-theoretic mapping using Cauchy-Schwarz quadratic mutual information. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 4791–4798 (2015)

    Google Scholar 

  7. Charrow, B., Kahn, G., Patil, S., Liu, S., Goldberg, K., Abbeel, P., Michael, N., Kumar, V.: Information-theoretic planning with trajectory optimization for dense 3D mapping. In: Proceedings of Robotics Science and Systems (2015)

    Google Scholar 

  8. Chen, L., Arambel, P.O., Mehra, R.K.: Estimation under unknown correlation: covariance intersection revisited. IEEE Trans. Autom. Control 47(11), 1879–1882 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  9. Feder, H.J.S., Leonard, J.J., Smith, C.M.: Adaptive mobile robot navigation and mapping. Int. J. Robot. Res. 18(7), 650–668 (1999)

    Article  Google Scholar 

  10. Huang, S., Kwok, N.M., Dissanayake, G., Ha, Q.P., Fang, G.: Multi-step look-ahead trajectory planning in SLAM: possibility and necessity. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1091–1096 (2005)

    Google Scholar 

  11. Jadidi, M.G., Miró, J.V., Valencia, R., Andrade-Cetto, J.: Exploration on continuous Gaussian process frontier maps. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 6077–6082 (2014)

    Google Scholar 

  12. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)

    Article  MATH  Google Scholar 

  13. Kaess, M., Dellaert, F.: Covariance recovery from a square root information matrix for data association. Robot. Auton. Syst. 57(12), 1198–1210 (2009)

    Article  Google Scholar 

  14. Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: iSAM2: Incremental smoothing and mapping using the Bayes tree. Int. J. Robot. Res. 31(2), 216–235 (2012)

    Article  Google Scholar 

  15. LaValle, S.M.: Rapidly-Exploring Random Trees: A New Tool for Path Planning (1998)

    Google Scholar 

  16. Makarenko, A.A., Williams, S.B., Bourgault, F., Durrant-Whyte, H.F.: An experiment in integrated exploration. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 534–539 (2002)

    Google Scholar 

  17. Martinez-Cantin, R., de Freitas, N., Doucet, A., Castellanos, J.A.: Active policy learning for robot planning and exploration under uncertainty. In: Proceedings of Robotics: Science and Systems, pp. 321–328 (2007)

    Google Scholar 

  18. Moravec, H.., Elfes, A.: High resolution maps from wide angle sonar. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 116–121 (1985)

    Google Scholar 

  19. Sim, R., Roy, N.: Global A-optimal robot exploration in slam. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1050–4729 (2005)

    Google Scholar 

  20. Stachniss, C., Grisetti, G., Burgard, W.: Information gain-based exploration using Rao-Blackwellized particle filters. In: Proceedings of Robotics: Science and Systems, pp. 65–72 (2005)

    Google Scholar 

  21. Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment—a modern synthesis. In: International Workshop on Vision Algorithms, pp. 298–372 (1999)

    Google Scholar 

  22. Valencia, R., Miró, J.V., Dissanayake, G., Andrade-Cetto, J.: Active pose SLAM. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1885–1891 (2012)

    Google Scholar 

  23. Vallvé, J., Andrade-Cetto, J.: Active pose SLAM with RRT*. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2167–2173 (2015)

    Google Scholar 

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Mechanical Engineering, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ, 07030, USA

    Jinkun Wang & Brendan Englot

Authors
  1. Jinkun Wang
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  2. Brendan Englot
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Corresponding author

Correspondence to Brendan Englot .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Nancy M. Amato

  2. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Greg Hager

  3. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Shawna Thomas

  4. Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

    Miguel Torres-Torriti

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Wang, J., Englot, B. (2020). Autonomous Exploration with Expectation-Maximization. In: Amato, N., Hager, G., Thomas, S., Torres-Torriti, M. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-28619-4_53

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