Terrain Segmentation with On-Line Mixtures of Experts for Autonomous Robot Navigation

  • Michael J. Procopio
  • W. Philip Kegelmeyer
  • Greg Grudic
  • Jane Mulligan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

We describe an on-line machine learning ensemble technique, based on an adaptation of the mixture of experts (ME) model, for predicting terrain in autonomous outdoor robot navigation. Binary linear models, trained on-line on images seen by the robot at different points in time, are added to a model library as the robot navigates. To predict terrain in a given image, each model in the library is applied to feature data from that image, and the models’ predictions are combined according to a single-layer (flat) ME approach. Although these simple linear models have excellent discrimination in their local area in feature space, they do not generalize well to other types of terrain, and must be applied carefully. We use the distribution of training data as the source of the a priori pointwise mixture coefficients that form the soft gating network in the ME model. Single-class Gaussian models are learned during training, then later used to perform density estimation of incoming data points, resulting in pointwise estimates of model applicability. The combined output given by ME thus permits models to abstain from making predictions for certain parts of the image. We show that this method outperforms a less sophisticated, non-local baseline method in a statistically significant evaluation using natural datasets taken from the domain.

Keywords

Mixture of Experts Classifier Ensembles Local Classifier Accuracy Online Learning Terrain Segmentation Autonomous Robot Navigation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jackel, L., Krotkov, E., Perschbacher, M., Pippine, J., Sullivan, C.: The DARPA LAGR program: Goals, challenges, methodology, and Phase I results. Journal of Field Robotics 23(11-12), 945–973 (2006)CrossRefGoogle Scholar
  2. 2.
    Howard, A., Turmon, M., Matthies, L., Tang, B., Angelova, A., Mjolsness, E.: Towards learned traversability for robot navigation: From underfoot to the far field. Journal of Field Robotics 23(11–12), 1005–1017 (2006)CrossRefGoogle Scholar
  3. 3.
    Procopio, M.J., Mulligan, J., Grudic, G.: Learning terrain segmentation with classifier ensembles for autonomous robot navigation in unstructured environments. Journal of Field Robotics 26(2), 145–175 (2009)CrossRefGoogle Scholar
  4. 4.
    Procopio, M.J., Mulligan, J., Grudic, G.: Long-term learning using multiple models for outdoor autonomous robot navigation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2007, pp. 3158–3165 (2007)Google Scholar
  5. 5.
    Procopio, M.J., Mulligan, J., Grudic, G.: Learning in dynamic environments with Ensemble Selection for autonomous outdoor robot navigation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2008, pp. 620–627 (2008)Google Scholar
  6. 6.
    Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation 3(1), 79–87 (1991)CrossRefGoogle Scholar
  7. 7.
    Jordan, M.I., Jacobs, R.A.: Hierarchical mixtures of experts and the EM algorithm. Neural Computation 6, 181–214 (1994)CrossRefGoogle Scholar
  8. 8.
    Jacobs, R.A.: Methods for combining experts’ probability assessments. Neural Computation 7(5), 867–888 (1995)CrossRefGoogle Scholar
  9. 9.
    Woods, K., Kegelmeyer, W., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Analysis and Machine Intelligence 19(4), 405–410 (1997)CrossRefGoogle Scholar
  10. 10.
    Sato, M.A., Ishii, S.: On-line EM algorithm for the normalized gaussian network. Neural Computation 12(2), 407–432 (2000)CrossRefGoogle Scholar
  11. 11.
    Moody, J., Darken, C.J.: Fast learning in networks of locally-tuned processing units. Neural Computation 1(2), 281–294 (1989)CrossRefGoogle Scholar
  12. 12.
    Grudic, G., Mulligan, J., Otte, M., Bates, A.: Online learning of multiple perceptual models for navigation in unknown terrain. In: FSR 2007: Proceedings of the International Conference on Field and Service Robotics (2007)Google Scholar
  13. 13.
    Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)MATHGoogle Scholar
  14. 14.
    Cox, D.R., Snell, E.J.: Analysis of Binary Data, 2nd edn. Chapman Hall, London (1989)MATHGoogle Scholar
  15. 15.
    Lin, C.J., Weng, R.C., Keerthi, S.S.: Trust region Newton method for large-scale logistic regression. J. Mach. Learn. Res. 9, 627–650 (2008), http://www.csie.ntu.edu.tw/~cjlin/liblinear/ MathSciNetMATHGoogle Scholar
  16. 16.
    Grudic, G., Mulligan, J.: Outdoor path labeling using polynomial mahalanobis distance. In: Proceedings of Robotics: Science and Systems (RSS), Philadelphia, PA (2006)Google Scholar
  17. 17.
    Procopio, M.J.: Hand-labeled DARPA LAGR datasets (2007), http://ml.cs.colorado.edu/~procopio/labeledlagrdata/
  18. 18.
    Hollander, M., Wolfe, D.A.: Nonparametric statistical methods, 2nd edn. Wiley-Interscience, New York (1999)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michael J. Procopio
    • 1
  • W. Philip Kegelmeyer
    • 1
  • Greg Grudic
    • 2
  • Jane Mulligan
    • 2
  1. 1.Sandia National LaboratoriesLivermoreUSA
  2. 2.Department of Computer ScienceUniversity of Colorado at BoulderBoulderUSA

Personalised recommendations