Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Vision Based Outdoor Terrain Classification for Unmanned Ground Vehicles

무인차량 적용을 위한 영상 기반의 지형 분류 기법

  • Published : 2009.04.01
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Abstract

For effective mobility control of unmanned ground vehicles in outdoor off-road environments, terrain cover classification technology using passive sensors is vital. This paper presents a novel method far terrain classification based on color and texture information of off-road images. It uses a neural network classifier and wavelet features. We exploit the wavelet mean and energy features extracted from multi-channel wavelet transformed images and also utilize the terrain class spatial coordinates of images to include additional features. By comparing the classification performance according to applied features, the experimental results show that the proposed algorithm has a promising result and potential possibilities for autonomous navigation.

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References

  1. R. Manduchi, A. Castano, A. Talukder, and L. Matthies, 'Obstacle detection and terrain classification for autonomous off-road navigation,' Autonomous Robot 18, pp. 81-102, Springer Science, 2005 https://doi.org/10.1023/B:AURO.0000047286.62481.1d
  2. P. Bellutta, R Manduchi, L. Matthies, K. Owens, and A. Rankin, 'Terrain perception for demo III,' Intelligent Vehicles Conference, 2000
  3. A. L. Rankin, C. F. Bergh, S. B. Goldberg, P. Bellutta, A. Huertas, and L. H. Matthies, 'Passive perception system for day/night autonomous off-road navigation,' SPIE Conf. of Autonomous Vehicles, 2005
  4. P. Jansen, W. van der Mark, J. C. van den Heuvel, and F. C. A. Groen, 'Colour based off-road environment and terrain type classification,' Proc. of the 8th Int'l IEEE Corf. on Intelligent Transportation Systems, Vienna, Austria, Sep. 2005 https://doi.org/10.1109/ITSC.2005.1520023
  5. R Castano, R Manduchi, and J. Fox, 'Classification experiments on real-world texture,' Workshop on Empirical Evaluation in Computer Vision, Kauai, HI, Dec. 2001
  6. G. Y. Sung, D. M. Kwak, D. J. Kim, and J. Lyoll, 'Terrain cover classification based on wavelet feature extraction,' Int'l Conf. on Control, Automation and Systems, Seoul, Korea, Oct. 2008
  7. I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, 1992
  8. I. Daubechies and W. Sweldens, 'Factoring wavelet transforms into lifting steps,' Journal of Fourier Analysis and Applications, vol. 4, no. 3, pp. 247-269, 1998 https://doi.org/10.1007/BF02476026
  9. M. Misiti, Y. Misiti, G. Oppenheim, and J. M. Poggi, 'Wavelet Toolbox User's Guide,' MathWork Inc
  10. 'Neural Network Toolbox,' MathWork Inc
  11. Y. Shima, T. Murakami, and M. Koga, 'A high speed algorithm for propagation-type labeling based on block sorting of runs in binary images,' Proc. 10th International Conference on Pattern Recognition, vol. 1, pp. 665-665, 1990