Traversable Region Estimation for Mobile Robots in an Outdoor Image

  • Sango Matsuzaki
  • Kimitoshi Yamazaki
  • Yoshitaka Hara
  • Takashi Tsubouchi


This paper describes a novel method to estimate appropriate traversable regions from an outdoor scene image. The traversable regions output by the proposed method reflect the common sense of people. For example, a candidate traversable region is “a paved road somewhat distant from the side ditch.” The input to the traversable region estimation is one color image. First, category is assigned to each pixel in the image. The categorization result is then input to the region estimator. Finally, the traversable region are estimated on the input image. An important aspect of this method is the application of two score functions in region estimation process. One score function places high value on categories selected as traversable paths by subjects. The other function places high value on categories that are not selected as traversable regions but are adjacent to categories with traversable paths. A combination of these two functions produces feasible estimation results. The effectiveness of the combined score functions was evaluated by experiments and a questionnaire.


Traversable region estimation Mobile robot Manually-instructed paths Learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Borenstein, J., Koren, Y.: Real-time obstacle avoidance for fast mobile robots. IEEE Trans. Syst. Man Cybern. 19(5), 1179–1187 (1989)CrossRefGoogle Scholar
  2. 2.
    Rimon, E., Koditschek, D.E.: Exact robot navigation using artificial potential functions. IEEE Trans. Robot. Autom. 8(5), 501–518 (1992)CrossRefGoogle Scholar
  3. 3.
    Connolly, C.I., Burns, J.B., Weiss, R.: Path planning using laplace’s equation. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 2102–2106 (1990)Google Scholar
  4. 4.
    Fraichard, T., Asama, H.: Inevitable collision states a step towards safer robots. Adv. Robot. 18(10), 1001–1024 (2004)CrossRefGoogle Scholar
  5. 5.
    Bogoslavskyi, I., Vysotska, O., Serafin, J., Grisetti, G., Stachniss, C.: Efficient traversability analysis for mobile robots using the Kinect sensor. In: IEEE European Conference on Mobile Robots, pp. 158–163 (2013)Google Scholar
  6. 6.
    Renner, A., Foehst, T., Berns, K.: Perception of environment properties relevant for off-road navigation. In: Autonome Mobile Systeme, pp. 201–208 (2009)Google Scholar
  7. 7.
    Hebert, M., Vandapel, N.: Terrain classification techniques from ladar data for autonomous navigation. In: Proceedings of The Collaborative Technology Alliances Conference, College Park (2003)Google Scholar
  8. 8.
    Mei, Y., Lu, Y.H., Lee, C., Hu, Y.C.: Energy-efficient motion planning for mobile robots. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), vol. 5, pp. 4344–4349 (2004)Google Scholar
  9. 9.
    Bradley, D., Unnikrishnan, R., Bagnell, J.: Vegetation detection for driving in complex environments. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 503–508 (2007)Google Scholar
  10. 10.
    Manduchi, R., Castano, A., Talukder, A., Matthies, L.: Obstacle detection and terrain classification for autonomous Off-Road navigation. Auton. Robot. 18(1), 81–102 (2005)CrossRefGoogle Scholar
  11. 11.
    Kim, D., Oh, S.M., Rehg, J.M.: Traversability classification for UGV navigation: a comparison of patch and superpixel representations. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3166–3173 (2007)Google Scholar
  12. 12.
    Wurm, K.M., Kretzschmar, H., Kummerle, R., Stachniss, C., Burgard, W.: Identifying vegetation from laser data in structured outdoor environments. Robot. Auton. Syst. 62(5), 675–684 (2012)CrossRefGoogle Scholar
  13. 13.
    Pereira, G.A.S., Pimenta, L.C.A., Fonseca, A.R., Correa, L.Q., Mesquita, R.C., Chaimowicz, L., Almeida, D.S.C., Campos, M.F.M.: Robot navigation in multi-terrain outdoor environments. Int. J. Robot. Res. 28(6), 685–700 (2009)CrossRefGoogle Scholar
  14. 14.
    Goto, K., Kon, K., Matsuno, F.: Motion planning of an autonomous mobile robot considering regions with velocity constraint. In: Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3269–3274 (2010)Google Scholar
  15. 15.
    Sisbot, E., Marin-Urias, L., Alami, R., Simeon, T.: A human aware mobile robot motion planner. IEEE Trans. Robot. 23(5), 874–883 (2007)CrossRefGoogle Scholar
  16. 16.
    Kirby, R., Simmons, R., Forlizzi, J.: COMPANION: a constraint-optimizing method for person-acceptable navigation. In: Proceedings of IEEE Symposium on Robot and Human Interactive Communication, pp. 607–612 (2009)Google Scholar
  17. 17.
    Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) 2, 524–531 (2005)Google Scholar
  18. 18.
    Akiyama, M., Kawano, Y., Yanai, K.: Object categorization by local feature matching with a large number of Web images. In: Proceedings of PCM Workshop on Multimedia Big Data Analytics (2012)Google Scholar
  19. 19.
    Nakayama, H., Harada, T., Kuniyoshi, Y.: Dense sampling low-level statistics of local features. In: ACM International Conference on Image and Video Retrieval, p. 17 (2009)Google Scholar
  20. 20.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  21. 21.
    Hunter, R.S.: Photoelectric color-difference meter. In: Proceedings of the Winter Meeting of the Optical Society of America 38(7), 661 (1958)Google Scholar
  22. 22.
    Scandolo, L., Fraichard, T.: An anthropomorphic navigation scheme for dynamic scenarios. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 809–814 (2011)Google Scholar
  23. 23.
    Guzzi, J., A Giusti, L.M., Gambardella, G., Theraulaz, G.: Caro: human-friendly robot navigation in dynamic environments. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 423–430 (2013)Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Sango Matsuzaki
    • 1
  • Kimitoshi Yamazaki
    • 2
  • Yoshitaka Hara
    • 3
  • Takashi Tsubouchi
    • 1
  1. 1.Graduate School of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan
  2. 2.Faculty of EngineeringShinshu UniversityNaganoJapan
  3. 3.Future Robotics Technology Center (fuRo)Chiba Institute of TechnologyNarashinoJapan

Personalised recommendations