Laser-Radar Data Fusion with Gaussian Process Implicit Surfaces

  • Marcos P. Gerardo-Castro
  • Thierry Peynot
  • Fabio Ramos
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 105)

Abstract

This work considers the problem of building high-fidelity 3D representations of the environment from sensor data acquired by mobile robots. Multi-sensor data fusion allows for more complete and accurate representations, and for more reliable perception, especially when different sensing modalities are used. In this paper, we propose a thorough experimental analysis of the performance of 3D surface reconstruction from laser and mm-wave radar data using Gaussian Process Implicit Surfaces (GPIS), in a realistic field robotics scenario. We first analyse the performance of GPIS using raw laser data alone and raw radar data alone, respectively, with different choices of covariance matrices and different resolutions of the input data. We then evaluate and compare the performance of two different GPIS fusion approaches. The first, state-of-the-art approach directly fuses raw data from laser and radar. The alternative approach proposed in this paper first computes an initial estimate of the surface from each single source of data, and then fuses these two estimates. We show that this method outperforms the state of the art, especially in situations where the sensors react differently to the targets they perceive.

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References

  1. 1.
    Brooker, G.: Sensors for Ranging and Imaging. SciTech Publishing, Inc. (2009)Google Scholar
  2. 2.
    Dragiev, S., Toussaint, M., Gienger, M.: Gaussian process implicit surfaces for shape estimation and grasping. In: IEEE International Conference on Robotics and Automation (2011)Google Scholar
  3. 3.
    El-Beltagy, M.A., Wright, W.A.: Gaussian processes for model fusion. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 376–383. Springer, Heidelberg (2001)Google Scholar
  4. 4.
    Gerardo-Castro, M.P., Peynot, T.: Laser-to-radar sensing redundancy for resilient perception in adverse environmental conditions. In: ARAA Australasian Conference on Robotics and Automation (2012)Google Scholar
  5. 5.
    Hollinger, G.A., Englot, B., Hover, F.S., Mitra, U., Sukhatme, G.S.: Active planning for underwater inspection and the benefit of adaptivity. International Journal of Robotics Research 32(1) (2013)Google Scholar
  6. 6.
    Kapoor, A., Ahn, H., Picard, R.: Mixture of gaussian processes for combining multiple modalities. In: International Workshop on Multiple Classifier Systems (2005)Google Scholar
  7. 7.
    O’Callaghan, S.T., Ramos, F.T., Durrant-Whyte, H.: Contextual occupancy maps incorporating sensor and location uncertainty. In: IEEE International Conference on Robotics and Automation (2010)Google Scholar
  8. 8.
    Ohtake, Y., Belyaev, A., Alexa, M., Turk, G., Seidel, H.: Multi-level partition of unity implicits. In: ACM SIGGRAPH Courses (2005)Google Scholar
  9. 9.
    Peynot, T., Scheding, S., Terho, S.: The Marulan Data Sets: Multi-Sensor Perception in Natural Environment with Challenging Conditions. International Journal of Robotics Research 29(13) (2010)Google Scholar
  10. 10.
    Peynot, T., Underwood, J., Scheding, S.: Towards reliable perception for unmanned ground vehicles in challenging conditions. In: IEEE/RSJ International Conference on Robotics and Intelligent Systems (2009)Google Scholar
  11. 11.
    Plagemann, C., Kersting, K., Pfaff, P., Burgard, W.: Gaussian beam processes: A nonparametric bayesian measurement model for range finders. In: Robotics: Science and Systems III (2007)Google Scholar
  12. 12.
    Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press (2006)Google Scholar
  13. 13.
    Whelan, T., et al.: Robust tracking for real-time dense rgb-d mapping with kintinuous. Technical Report MIT-CSAIL-TR-2012-031, CSAIL, MIT (2012)Google Scholar
  14. 14.
    Thrun, S., Burgard, W., Fox, D.: A real-time algorithm for mobile robot mapping with applications to multi-robot and 3d mapping. In: IEEE International Conference on Robotics and Automation (2000)Google Scholar
  15. 15.
    Turk, G., O’brien, J.F.: Variational implicit surfaces. Technical Report GIT-GVU-99-15, Georgia Institute of Technology (1999)Google Scholar
  16. 16.
    Underwood, J.P., Hill, A., Peynot, T., Scheding, S.J.: Error modeling and calibration of exteroceptive sensors for accurate mapping applications. Journal of Field Robotics 27(1) (2010)Google Scholar
  17. 17.
    Vasudevan, S.: Data fusion with gaussian processes. Robotics and Autonomous Systems 60(12) (2012)Google Scholar
  18. 18.
    Vieira, A.W., Drews, P.L.J., Campos, M.F.M.: Efficient change detection in 3d environment for autonomous surveillance robots based on implicit volume. In: IEEE International Conference on Robotics and Automation (2012)Google Scholar
  19. 19.
    Williams, O., Fitzgibbon, A.: Gaussian process implicit surfaces. In: Gaussian Processes in Practice Workshop (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marcos P. Gerardo-Castro
    • 1
  • Thierry Peynot
    • 1
  • Fabio Ramos
    • 2
  1. 1.Australian Centre for Field Robotics (ACFR)The University of SydneySydneyAustralia
  2. 2.ACFR, School of Information TechnologiesThe University of SydneySydneyAustralia

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