1 / 13

Terrain Classification Based On Structure For Autonomous Navigation in Complex Environments

II. Signal Processing And Application. 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

snow
Download Presentation

Terrain Classification Based On Structure For Autonomous Navigation in Complex Environments

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. II. Signal Processing And Application Terrain Classification Based On Structure For Autonomous Navigation in Complex Environments Duong V.Nguyen1, Lars Kuhnert2, Markus Ax2, and Klaus-Dieter Kuhnert2 1Research School MOSES, University of Siegen, Germany 2Institute for Real-Time-Learning Systems, University of Siegen, Germany

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

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

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

  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? Introduction Advantages: • Stable data acquisition • High precision • Affordable

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

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

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

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

  10. Experiments and Results

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

  12. Thank you for your attention ! Q&A

  13. 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, 2007. [2] Matthias Plaue, Analysis of the PMD Imaging System, Technical Report,Berlin, Germany, 2006. [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-2010. [4] John Tuley, Nicolas Vandapel, and Martial Hebert, Technical report CMU-RI-TR-04-44, Robotics Institute, Carnegie Mellon University, August, 2004. [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 839 -861, Oct-2006. [6] Pedro F. Felzenszwalb, Daniel P. Huttenlocher , Efficient Graph-Based Image Segmentation, IJCV, Volume 59 Issue 2, Sept-2004. [7] R. Willstatter and A. Stoll, Utersuchungenuber Chlorophyll, Berlin: Springer, 1913. [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, 2004. [9] Huang, J., Lee, A. Mumford, D. Statistics of Range Images, ICVPR, Los Alamitos, CA, USA, 2000. [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), 2004. [12] C. Cortes, V. Vapnik, Support vector networks. Machine Learning, vol.20, p. 273-297, 1995. [13] Quinlan, J. 1993. 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-2000. [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. 55. 1997. [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, 1997. [17] Baudat. G and Anouar. F Kernel-Based Methods and Function Approximation, International Joint Conference on Neural Networks, USA, pp. 1244 - 1249, 2001.

More Related