Boosting support vector machines for RGB-D based terrain classification

• Autorzy: Wietrzykowski J. , Belter D.

Identyfikatory

DOI

10.14313/JAMRIS_3-2014/24

• Języki publikacji: EN

Abstrakty

EN

This paper deals with the terrain classification problem for an autonomous mobile robot. The robot is designed to operate in an outdoor environment. The classifier integrates data from RGB camera and 2D laser scanner. The camera provides information about visual appearance of the objects in front of the robot. The laser scanner provides data about distance to the objects and their ability to reflect infrared beam. In this paper we present the method which create terrain segments and classifies them using joint application of Support Vector Machine (SVM) classifier and AdaBoost algorithm. The classifica- tion results of the experimental verification are provided in the paper.

Słowa kluczowe

EN

terrain classification; mobile robot; RGB-D

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2014
• Tom: Vol. 8, No. 3
• Strony: 28--34
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Wietrzykowski J.

• Poznań University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3A, 60-965 Poznań, Poland

autor

Belter D.

• Poznań University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3A, 60-965 Poznań, Poland

Bibliografia

• [1] A. Angelowa, L. Matthies, D. Helmick, and P. Perona, “Fast terrain classifiication using variable-length representation for autonomous navigation”. In: Proceedings of the conference on Computer Vision and Pattern Recognition,Minneapolis, USA, 2007, pp. 1–8, doi:10.1109/CVPR.2007.383024.
• [2] D. Benbouzid, R. Busa-Fekete, N. Casagrande,F.-D. Collin, and B. Kegl, “Multiboost: a multipurpose boosting package”, Journal of Machine Learning Research, vol. 13, 2012, pp. 549–553.
• [3] C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
• [4] J.-Y. Bouguet. “Camera calibration toolbox for matlab”, 2014,www.vision.caltech.edu/bouguetj/calib_doc.Journal of Automation, Mobile Robotics & Intelligent Systems VOLUME 8, N# 3 2014
• [5] J. Chetan, M. Krishna, and C. Jawahar, “Fast and spatially-smooth terrain classification using monocular camera”. In: Proceedings of 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey, 2010, pp. 4060–4063.
• [6] H. Dahlkamp, A. Kaehler, D. Stavens, S. Thrun, and G. Bradski, “Self-supervised monocular road detection in desert terrain”. In: Proceedings of Robotics: Science and Systems, Philadelphia, USA, 2006.
• [7] T. Dietterich, “An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization”, Machine Learning, vol. 40, no. 2, 2000, pp. 139–157.
• [8] P. Felzenszwalb and D. Huttenlocher, “Efficient graph-based image segmentation”,International Journal of Computer Vision,vol. 59, no. 2, 2004, pp. 167–181, doi:10.1023/B:VISI.0000022288.19776.77.
• [9] P. Filitchkin and K. Byl, “Feature-based terrain classification for littledog”. In: Proceedings of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems,Vilamoura, Portugal, 2012, pp. 1387–1392,doi: 10.1109/IROS.2012.6386042.
• [10] E. Garcı́a and F. Lozano, “Boosting support vector machines”. In: Proceedings of 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, 2007,pp. 153–167.
• [11] I. Halatci, C. Brooks, and K. Iagnemma, “Terrain classification and classifier fusion for planetary exploration rovers”. In: Proceedings of 2007 IEEE Aerospace Conference, Big Sky, USA, 2007, pp.1–11, doi: 10.1109/AERO.2007.352692.
• [12] M. Hoepflinger, C. Remy, M. Hutter, L. Spinello,and R. Siegwart, “Haptic terrain classification for legged robots”. In: Proceedings of 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage,USA, 2010, pp. 2828–2833, doi:10.1109/ROBOT.2010.5509309.
• [13] R. Karlsen and G. Witus, “Terrain understanding for robot navigation”. In: Proceedings of 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Diego, USA, 2007,pp. 895–900, doi: 10.1109/IROS.2007.4399223.
• [14] Y. Khan, P. Komma, and A. Zell, “High resolution visual terrain classification for outdoor robots”.In: Proceedings of IEEE International Conference on Computer Vision Workshops (ICCV Workshops),Barcelona, Spain, 2011, pp. 1014–1021,doi: 10.1109/ICCVW.2011.6130362.
• [15] S. Laible, Y. Khan, and A. Zell, “Terrain classification with conditional random fields on fused 3d lidar and camera data”. In: Proceedings of European Conference on Mobile Robots, Barcelona, Spain, 2013, pp. 172–177, doi:10.1109/ECMR.2013.6698838.
• [16] J. Lobo and J. Dias, “Relative pose calibration between visual and inertial sensors”,International Journal of Robotics Research,vol. 26, no. 6, 2004, pp. 561–575, doi:10.1177/0278364907079276.
• [17] D. Maier, C. Stachniss, and M. Bennewitz,“Vision-based humanoid navigation using selfsupervised obstacle detection”, International Journal of Humanoid Robotics, vol. 10, no. 2,2013, doi: 10.1142/S0219843613500163.
• [18] R. Schapire and Y. Singer, “Improved boosting algorithms using confidence-rated predictions”,Machine Learning, vol. 37, 1999, pp. 297–336,doi: 10.1145/279943.279960.
• [19] K. Walas, A. Schmidt, M. Kraft, and M. Fularz, “Hardware implementation of ground classification for a walking robot”. In: Proceedings of the 9th International Workshop on Robot Motion and Control, Wąsowo, Poland, 2013, pp. 110–115,doi: 10.1109/RoMoCo.2013.6614594.
• [20] Q. Zhang and R. Pless, “Extrinsic calibration of a camera and laser range finder”. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, 2004, pp. 2301–2306, doi: 10.1016/j.proeng.2012.01.669.