single-rb.php

JRM Vol.24 No.1 pp. 16-27
doi: 10.20965/jrm.2012.p0016
(2012)

Paper:

Self-Supervised Online Long-Range Road Estimation in Complicated Urban Environments

Yoji Kuroda, Masataka Suzuki, Teppei Saitoh,
and Eisuke Terada

Department of Mechanical Engineering, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan

Received:
January 26, 2011
Accepted:
April 13, 2011
Published:
February 20, 2012
Keywords:
road perception, robot vision, terrain classification, level-set method, self-supervised learning
Abstract

In this paper, we propose a long-range road estimation method for autonomousmobile robots in unstructured urban environments. Near-range road surface conditions are estimated by using remission value as reflectivity of a laser scanner. Graph cut algorithm is applied to estimate road region robustly also in complicated environments. Moreover, we propose a novel image segmentation method to estimate long-range road surface. A compact texture/color feature is integrated with level-set method to estimate precise road boundaries robustly. Our proposed image segmentation approach gives better performance compared with standard classification approach. Finally, we run our autonomous mobile robot in “Tsukuba Challenge 2009” and our university campus, and experimental results have shown a marked increase accuracy in road estimation over standard methods.

Cite this article as:
Y. Kuroda, M. Suzuki, T. Saitoh, and <. Terada, “Self-Supervised Online Long-Range Road Estimation in Complicated Urban Environments,” J. Robot. Mechatron., Vol.24, No.1, pp. 16-27, 2012.
Data files:
References
  1. [1] H. Dahlkamp, A. Kaehler, D. Stavens, S. Thrun, and G. Bradski, “Self-supervised monocular road detection in desert terrain,” Robotics: Science & Systems, 2006.
  2. [2] L. Jackel, E. Krotkov, M. Perschbacher, J. Pippine, and C. Sullivan, “The DARPA LAGR program: Goals, challenges, methodology, and Phase I results,” J. of Field Robotics, Vol.23, pp. 945-973, November/December 2006.
  3. [3] M. Yoichi, C. Alexander, E. Takeuchi, A. Aburadani, and T. Tsubouchi “Autonomous Robot Navigation in Outdoor Cluttered Pedestrian Walkways,” J. of Field Robotics, 2009.
  4. [4] S. Yuta, H. Hashimoto, and H. Tashiro, “Tsukuba Challenge – Real World Robot Challenge (RWRC): Toward actual autonomous robots in our daily life,” The 25th Annual Conf. of the Robotics Society of Japan, 3D19, 2007.
  5. [5] T. Saitoh and Y. Kuroda, “Self-Supervised Mapping for Road Shape Estimation Using Laser Remission in Urban Environments,” J. of Robotics and Mechatronics, Vol.22, No.6, pp. 726-736, Dec. 2010.
  6. [6] K. M. Wurm, R. Kümmerle, C. Stachniss, and W. Burgard, “Improving Robot Navigation in Structured Outdoor Environments by Identifying Vegetation from Laser Data,” In Proc. of the IEEE/RSJ Int. Conf. on IROS, 2009.
  7. [7] I. Ulrich and I. Nourbakhsh, “Appearance-Based Obstacle Detection with Monocular Color Vision,” AAAI Conf., pp. 866-871, 2000.
  8. [8] G. Bradski, A. Kaehler, and V. Pisarevsky, “Learning-based computer vision with Intel’s open source computer vision library,” Intel Technol. J., Vol.9, No.2, pp. 119-130, May 2005.
  9. [9] D. Kim, S. Oh, and J.M. Rehg, “Traversability classification for ugv navigation: A comparison of patch and superpixel representations,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), (San Diego, CA), Oct. 2007.
  10. [10] P. Felzenszwalb and D. P. Huttenlocher, “Efficient Graph-Based Image Segmentation,” Int. J. of Computer Vision, Vol.59, pp. 167-181, 2004.
  11. [11] S. Ghosh and J. Mulligan, “A Segmentation Guided Label Propagation Scheme for Autonomous Navigation,” IEEE Int. Conf. on Robotics and Automation, 2010.
  12. [12] R. Hadsell, P. Sermanet, M. Scoffier, A. Erkan, K. Kavukcuoglu, U. Muller, and Y. LeCun, “Learning long-range vision for autonomous off-road driving,” J. of Field Robotics, 2009.
  13. [13] M. Bajracharya, A. Howard, L. H. Matthies, B. Tang, and M. Turmon, “Autonomous Off-Road Navigation with End-to-End Learning for the LAGR Program,” J. of Field Robotics, 2009.
  14. [14] M. J. Procopio, J. Mulligan, and G. Grudic, “Coping with Imbalanced Data for Improved Terrain Prediction in Autonomous Outdoor Robot Navigation,” IEEE Int. Conf. on Robotics and Automation, 2010.
  15. [15] F. W. Rauskolb, K. Berger, C. Lipski et al., “Caroline: An Autonomously Driving Vehicle for Urban Environments,” J. of Field Robotics, Vol.23, No.3, pp. 161-182, October 2007.
  16. [16] B. Douillard, A. Brooks, F. T. Ramos, and H. F. Durrant-Whyte, “Combining Laser and Vision for 3D Urban Classification,” Proc. Neural Information Processing Systems Conference (NIPS) 2009, workshop: Learning from Multiple Sources with Applications to Robotics, 2009.
  17. [17] I. Posner, D. Schroeter, and P. Newman “Online generation of scene descriptions in urban environments,” Robotics and Autonomous Systems, Vol.56, No.11, pp. 901-914, Semantic Knowledge in Robotics, 2008.
  18. [18] I. Posner, M. Cummins, and P. Newman, “A generative framework for fast urban labeling using spatial and temporal context,” Vol.26 Issue 2-3, April 2009.
  19. [19] M. Varma and A. Zisserman, “Texture classification: Are filter banks necessary?” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Vol.2, pp. 691-696, 2003.
  20. [20] T. Ojala, M. Pietikäinen, and D. Harwood, “A Comparative Study of Texture Measures with Classification Based on Feature Distributions,” Pattern Recognition, Vol.29, pp. 51-59, 1996.
  21. [21] M. Blas, M. Agrawal, K. Konolige, and S. Aravind, “Fast color/texture segmentation for outdoor robots,” in Proc. Int. Conf. Intelligent Robots and Systems, 2008.
  22. [22] Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images,” In Int. Conf. on Computer Vision, Vol.1, pp. 105-112, July 2001.
  23. [23] Y. Iwashita, R. Kurazume, K. Hara, T. Tsuji, and T. Hasegawa, “Fast Implementation of Level Set Method and Its Real-time Applications,” Proc. 2004 IEEE Int. Conf. on Systems, Man, and Cybernetics, 2004.
  24. [24] S. Liapis, E. Sifakis, and G. Tziritas, “Color and/or Texture Segmentation using Deterministic Relaxation and Fast Marching Algorithms,” Proc. Int. Conf. on Pattern Recognition, Vol.3, pp. 621-624, Sep. 2000.
  25. [25] S. Thrun, M. Montemerlo, and A. Aron, “Probabilistic Terrain Analysis For High-Speed Desert Driving,” Proc. Robotics Science and Systems, Philadelphia, PA, USA, August 16-19, 2006.
  26. [26] M. Z. Brown, D. Burschka, and G. D. Hager, “Advances in computational stereo,” TPAMI, Vol.25, No.8, pp. 993-1008, 2003.
  27. [27] Y. Boykov and V. Komolgorov, “An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision,” Proc. Third Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2001.
  28. [28] S. Osher and J. A. Sethian, “Fronts propagating with curvaturedependent speed: Algorithms based on Hamilton-Jacobi formulations,” J. of Computational Physics, Vol.79, Issue 1, pp. 12-49, November 1988.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Jul. 04, 2020