1. 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

2. 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

3. 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.

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

5. 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

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

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

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

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

10. Experiments and Results

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

12. Q&A

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