Terrain Classification Based On Structure For Autonomous Navigation in Complex Environments Duong V.Nguyen 1, Lars Kuhnert 2, Markus Ax 2, and Klaus-Dieter Kuhnert 2 1 Research School MOSES, University of Siegen, Germany 2 Institute for Real-Time-Learning Systems, University of Siegen, Germany II. Signal Processing And Application

Introduction Methodology Graph-Cut Feature Extraction Neighbor Distance Variation Inside Edgeless Areas Conditional Local Point Statistics Support Vector Machine Experiments and Results Conclusion Reference Outline

Introduction Variety of terrain Avoid obstacles Maintain rollover stability Manage power …etc Why do we need Terrain Classification?  autonomous operation Or: complete task without direct control by a human Bomb-defusing Vacuum cleaning Forest exploration …etc What is unmanned system ? AMOR: 1 st prize of innovation awards, ELROB- 2010, Hammelburg, Germany.

PMD camera Laser Scanner Stereo Cameras Introduction Recent 3-D Approaches

Problems:  Beam scattering effects  Only used for static scenes  Object detection purely based on structure is not really robust in some scenes. Solutions:  Local points statistic analysis (Graph-Cut for depth image segmentation)  Gaussian Mixture Model using Expectation maximization  Combining 3-D and 2-D features Why should Laser Scanner be used? Advantages:  Stable data acquisition  High precision  Affordable Introduction

Classifier SVM ROI extraction 3-D point cloud 3-D Features Depth image segmentation Methodology Terrain Classification System Diagram

Graph-Cut Technique Methodology Internal difference Component difference Un-Joint Condition:

Classifier SVM ROI extraction 3-D point cloud 3-D Features Depth image segmentation Methodology Feature Extraction

Classifier SVM ROI extraction 3-D point cloud 3-D Features Depth image segmentation Methodology Support Vector Machine

Experiments and Results

Graph-cut Technique For Segmentation Neighbor Distance Variation Feature Conditional Local Point Statistics Feature Future work: 2D&3D Calibration Color Features Conclusion

Q&A

References [1] David Bradley, Ranjith Unnikrishnan, and J. Andrew (Drew) Bagnell, Vegetation Detection for Driving in Complex Environments, IEEE International Conference on Robotics and Automation, April, [2] Matthias Plaue, Analysis of the PMD Imaging System, Technical Report,Berlin, Germany, [3] Duong V.Nguyen, Lars Kuhnert, Markus Ax, and Klaus-Dieter Kuhnert,Combining distance and modulation information for detecting pedestrians in outdoor environment using a PMD camera, Proc. on the 11th IASTED International Conference Computer Graphics and Imaging, Innsbruck, Austria, Feb [4] John Tuley, Nicolas Vandapel, and Martial Hebert, Technical report CMU-RI-TR-04-44, Robotics Institute, Carnegie Mellon University, August, [5] J. F. Lalonde, N. Vandapel, D. F. Huber, M. Hebert, Natural Terrain Classification using Three-Dimensional Ladar Data for Ground Robot Mobility, Journal of Field Robotics, Volume 23 Issue 10, Pages , Oct [6] Pedro F. Felzenszwalb, Daniel P. Huttenlocher, Efficient Graph-Based Image Segmentation, IJCV, Volume 59 Issue 2, Sept [7] R. Willstatter and A. Stoll, Utersuchungenuber Chlorophyll, Berlin: Springer, [8] Anguelov, D., Taskar, B., Chatalashev, V., Koller, D., Gupta, D., Heitz, G. Ng, A.,Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data, ICVPR, San Diego, CA, USA, [9] Huang, J., Lee, A. Mumford, D. Statistics of Range Images, ICVPR, Los Alamitos, CA, USA, [10] Rasmussen, C., Combining Laser Range, Color and Texture Cues for Autonomous Road Following, ICRA, Washington, DC, USA. [11] N. Vandapel and M. Herbert, Natural terrain classification using 3-d ladar data, in IEEE Int. Conf. on Robotics and Automation (ICRA), [12] C. Cortes, V. Vapnik, Support vector networks. Machine Learning, vol.20, p , [13] Quinlan, J C4.5: Programs for Machine Learning. Morgan Kaufmann: San Mateo, CA. [14] Guoqiang Peter Zhang, Neural Networks for Classification: A Survey, IEEE Trans. on Systems, Man, and Cybernetics, Vol. 30, No. 4, Nov [15] Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, no [16] Bilmes, J., A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models, Berkeley, CA, USA: The International Computer Science Institute, University of Berkeley, Technical Report, [17] Baudat. G and Anouar. F Kernel-Based Methods and Function Approximation, International Joint Conference on Neural Networks, USA, pp , 2001.