Support Vector Machines and Features for Environment Perception in Mobile Robotics

  • Rui Araújo
  • Urbano Nunes
  • Luciano Oliveira
  • Pedro Sousa
  • Paulo Peixoto
Part of the Studies in Computational Intelligence book series (SCI, volume 137)

Abstract

Environment perception is one of the most challenging and underlying task which allows a mobile robot to perceive obstacles, landmarks and extract useful information to navigate safely. In this sense, classification techniques applied to sensor data may enhance the way mobile robots sense their surroundings. Amongst several techniques to classify data and to extract relevant information from the environment, Support Vector Machines (SVM) have demonstrated promising results, being used in several practical approaches. This chapter presents the core theory of SVM, and applications in two different scopes: using Lidar (Light Detection and Ranging) to label specific places, and vision-based human detection aided by Lidar.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Almeida, J., Almeida, A., Araújo, R.: Tracking Multiple Moving Objects for Mobile Robotics Navigation. In: Proc. 10th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2005) (2005)Google Scholar
  2. 2.
    Araújo, R.: Prune-Able Fuzzy ART Neural Architecture for Robot Map Learning and Navigation in Dynamic Environments. IEEE Transactions on Neural Networks 17(5), 1235–1249 (2006)CrossRefGoogle Scholar
  3. 3.
    Arnulf, G., Silvio, B.: Normalization in Support Vector Machines. In: Radig, B., Florczyk, S. (eds.) DAGM 2001. LNCS, vol. 2191, pp. 277–282. Springer, Heidelberg (2001)Google Scholar
  4. 4.
    Bianchini, M., Gori, M., Maggini, M.: On the Problem of Local Minima in Recurrent Neural Networks. IEEE Transaction on Neural Networks, Special Issue on Dynamic Recurrent Neural Networks, 167–177 (1994)Google Scholar
  5. 5.
    Borges, G., Aldon, M.: Line Extraction in 2D Range Images for Mobile Robotics. Journal of Intelligent and Robotic Systems 40(3), 267–297 (2004)CrossRefGoogle Scholar
  6. 6.
    Burges, C.J.: A Tutorial on Support Vector Machines for Pattern. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  7. 7.
    Costa, J., Dias, F., Araújo, R.: Simultaneous Localization and Map Building by Integrating a Cache of Features. In: Proc. 11th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2006) (2006)Google Scholar
  8. 8.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machine and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)Google Scholar
  9. 9.
    Duanl, K.-B., Sathiya Keerthi, S.: Which Is the Best Multiclass SVM Method? An Empirical Study. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 278–285. Springer, Heidelberg (2005)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: International Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  11. 11.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. Book News Inc. (2000)Google Scholar
  12. 12.
    Garcia, C., Delakis, M.: Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection. Proceedings of the IEEE 26(11), 1408–1423 (2004)Google Scholar
  13. 13.
    Gonzalez, R., Woods, R.: Digital Image Processing. Addison-Wesley, Reading (1993)Google Scholar
  14. 14.
    Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.: Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Springer, Heidelberg (2006)MATHGoogle Scholar
  15. 15.
    Hearst, M.A., Dumais, S.T., Osman, E., Platt, J., Scholkopf, B.: Support Vector Machines. IEEE Intelligent Systems 13(4), 18–28 (1998)CrossRefGoogle Scholar
  16. 16.
    Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural Networks for Short-term Load Forecasting: a Review and Evaluation. IEEE Trans. on Power Systems 16(1), 44–55 (2001)CrossRefGoogle Scholar
  17. 17.
    Hokuyo: Range-Finder Type Laser Scanner URG-04LX Specifications, Hokuyo Automatic Co. (2005)Google Scholar
  18. 18.
    Hsu, C., Lin, C.: A Comparison Methods for Multi-Class Support Vector Machine. IEEE Transactions on Neural Networks 13, 415–425 (2002)CrossRefGoogle Scholar
  19. 19.
    Joachims, T.: Optimizing Search Engines Using Clickthrough Data. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, New York (2002)Google Scholar
  20. 20.
    Kecman, V.: Learning and Soft Computing. MIT Press, Cambridge (2001)MATHGoogle Scholar
  21. 21.
    Lawrence, S., Giles, L., Tsoi, A., Back, A.: Face Recognition: A Convolutional Neural Network Approach. IEEE Transactions on Neural Networks, Special Issue on Neural Network and Pattern Recognition 8(1), 98–113 (1997)Google Scholar
  22. 22.
    LeCunn, Y., Bengio, L., Haffner, P.: Gradient-based Learning Applied to Document Recognition. Journal of Neurophysiology, Proceedings of the IEEE 86(11), 2278–2324 (1998)Google Scholar
  23. 23.
    Li, H., Qi, F., Wang, S.: A Comparison of Model Selection Methods for Multi-class Support Vector Machines. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3483, pp. 1140–1148. Springer, Heidelberg (2005)Google Scholar
  24. 24.
    Loncaric, S.: A Survey of Shape Analysis Techniques. Pattern Recognition Journal (1998)Google Scholar
  25. 25.
    Lowe, D.: Distinctive Image Features From Scale-invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  26. 26.
    McInerney, J., Haines, K., Biafore, S., Hecht-Nielsen: Back Propagation Error Surfaces Can Have Local Minima. In: International Joint Conference on Neural Networks (IJCNN) (1989)Google Scholar
  27. 27.
    Mozos, O.M., Stachniss, C., Rottmann, A., Burgard, W.: Using AdaBoost for Place Labeling and Topological Map Building. In: Robotics Research: Results of the 12th International Symposium ISRR, pp. 453–472 (2007)Google Scholar
  28. 28.
    Mozos, O.M., Stachniss, C., Burgard, W.: Supervised Learning of Places from Range Data Using Adaboost. In: IEEE International Conference Robotics and Automation, April 2005, pp. 1742–1747 (2005)Google Scholar
  29. 29.
    Munder, S., Gavrila, M.: An Experimental Study on Pedestrian Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1863–1868 (2006)CrossRefGoogle Scholar
  30. 30.
    Nunes, U., Fonseca, J.A., Almeida, L., Araújo, R., Maia, R.: Using Distributed Systems in Real-Time Control of Autonomous Vehicles. In: ROBOTICA, May-June 2003, vol. 21(3), pp. 271–281. Cambridge University Press, Cambridge (2003)Google Scholar
  31. 31.
    Online. Acessed in: http://svmlight.joachims.org/
  32. 32.
  33. 33.
  34. 34.
    Online. Acessed in: http://www.kernel-machines.org/
  35. 35.
  36. 36.
    Oliveira, L., Nunes, U., Peixoto, P.: A Nature-inspired Model for Partially Occluded Pedestrian Recognition with High Accuracy. IEEE Transactions on Intelligent Transportation Systems (submitted, 2007)Google Scholar
  37. 37.
    Oren, M., Papageorgiou, C., Shina, P., Poggio, T.: Pedestrian Detection Using Wavelet Templates. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 193–199 (1997)Google Scholar
  38. 38.
    O’Rourke, J.: Computational Geometry in C, 2nd edn. Cambridge University Press, Cambridge (1998)MATHGoogle Scholar
  39. 39.
    Papageorgiou, C., Poggio, T.: Trainable Pedestrian Detection. In: International Conference on Image Processing, vol. 4, pp. 35–39 (1999)Google Scholar
  40. 40.
    Pires, G., Nunes, U.: A Wheelchair Steered through Voice Commands and Assisted by a Reactive Fuzzy-Logic Controller. Journal of Intelligent and Robotic Systems 34(3), 301–314 (2002)CrossRefMATHGoogle Scholar
  41. 41.
    Platt, J.: Using sparseness and analytic qp to speed training of support vector machines, Neural Information Processing Systems (1999), http://research.microsoft.com/users/jplatt/smo.html
  42. 42.
    Premebida, C., Nunes, U.: A Multi-Target Tracking and GMM-Classifier for Intelligent Vehicles. In: Proc. 9th IEEE Int. Conf. on Intelligent Transportation Systems, Toronto, Canada (2006)Google Scholar
  43. 43.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipies, 3rd edn., September 2007. Cambridge University Press, Cambridge (2007)Google Scholar
  44. 44.
    Serre, T., Wolf, L., Poggio, T.: Object Recognition with Features Inspired by Visual Cortex. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 994–1000 (2005)Google Scholar
  45. 45.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)Google Scholar
  46. 46.
    Smola, A., Schölkopf, B.: A Tutorial on Support Vector Regression, NeuroCOLT2 Technical Report NC2-TR, pp. 1998–2030 (1998)Google Scholar
  47. 47.
    Sousa, P., Araújo, R., Nunes, U., Alves, L., Lopes, A.C.: Real-Time Architecture for Mobile Assistant Robots. In: Proc. IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2007), Patras, Greece, September 25-28, 2007, pp. 965–972 (2007)Google Scholar
  48. 48.
    Stachniss, C., Mozos, O.M., Burgard, W.: Speeding-Up Multi-Robot Exploration by Considering Semantic Place Information. In: Proc. the IEEE Int.Conf.on Robotics & Automation (ICRA), Orlando, USA (2006)Google Scholar
  49. 49.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2005)MATHGoogle Scholar
  50. 50.
    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
  51. 51.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)MATHGoogle Scholar
  52. 52.
    Young, A.: Handbook of Pattern Recognition and Image Processing. Academic Press, London (1986)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rui Araújo
    • 1
  • Urbano Nunes
    • 1
  • Luciano Oliveira
    • 1
  • Pedro Sousa
    • 1
  • Paulo Peixoto
    • 1
  1. 1.ISR-Institute of Systems and Robotics, and Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbraPortugal

Personalised recommendations