DOI QR코드

DOI QR Code

Obstacle Detection Algorithm Using Forward-Viewing Mono Camera

전방 모노카메라 기반 장애물 검출 기술

  • Lee, Tae-Jae (School of Electrical and Computer Engineering, Automation and Systems Research Institute (ASRI), Biomimetic Robot Research Center, Seoul National University) ;
  • Lee, Hoon (School of Electrical and Computer Engineering, Automation and Systems Research Institute (ASRI), Biomimetic Robot Research Center, Seoul National University) ;
  • Cho, Dong-Il Dan (School of Electrical and Computer Engineering, Automation and Systems Research Institute (ASRI), Biomimetic Robot Research Center, Seoul National University)
  • 이태재 (서울대학교 전기정보공학부, 자동화시스템공동연구소, 국방생체모방자율로봇 특화연구센터) ;
  • 이훈 (서울대학교 전기정보공학부, 자동화시스템공동연구소, 국방생체모방자율로봇 특화연구센터) ;
  • 조동일 (서울대학교 전기정보공학부, 자동화시스템공동연구소, 국방생체모방자율로봇 특화연구센터)
  • Received : 2015.06.09
  • Accepted : 2015.08.03
  • Published : 2015.09.01

Abstract

This paper presents a new forward-viewing mono-camera based obstacle detection algorithm for mobile robots. The proposed method extracts the coarse location of an obstacle in an image using inverse perspective mapping technique from sequential images. In the next step, graph-cut based image labeling is conducted for estimating the exact obstacle boundary. The graph-cut based labeling algorithm labels the image pixels as either obstacle or floor as the final outcome. Experiments are performed to verify the obstacle detection performance of the developed algorithm in several examples, including a book, box, towel, and flower pot. The low illumination condition, low color contrast between floor and obstacle, and floor pattern cases are also tested.

Keywords

References

  1. H. Lee, T. J. Lee, and D. I. Cho, "Forward-viewing monocamera based obstacle detection algorithm for biomimetic micro ground robots," Proc. of ICROS Annual Conference (in Korean), Daejeon, Korea, pp. 293-294, May 2015.
  2. H. Xiao, Z. Li, C. Yang, W. Yuan, and L. Wang, "RGB-D sensor-based visual target detection and tracking for an intelligent wheelchair robot in indoors environments," International Journal of Control, Automation and Systems, vol. 13, no. 3, pp. 521-529, 2015. https://doi.org/10.1007/s12555-014-0353-4
  3. S. E. Lee and B. K. Kim, "3D simultaneous localization and map building (SLAM) using a 2D laser range finder based on vertical/horizontal planar polygons," Journal of Institute of Contorl, Robotics and Systems (in Korean), vol. 20, no. 11, pp. 1153-1163, 2014. https://doi.org/10.5302/J.ICROS.2014.14.0027
  4. G. Fu, P. Corradi, A. Menciassi, and P. Dario, "An integrated triangulation laser scanner for obstacle detection of miniature mobile robots in indoor environment," IEEE/ASME Transactions on Mechatronics, vol. 16, no. 4, pp. 778-783, 2011. https://doi.org/10.1109/TMECH.2010.2084582
  5. S. J. Yoon, K. S. Roh, and Y. B. Shim, "Vision-based obstacle detection and avoidance: Application to robust indoor navigation of mobile robots," Advanced Robotics, vol. 22, no. 4, pp. 477-492, 2008. https://doi.org/10.1163/156855308X10.1163/156855308X294699
  6. D. H. Lee, H. J. Kim and H. Myung. "Image feature-based realtime RGB-D 3D SLAM with GPU acceleration." Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 5, pp. 457-461, 2013. https://doi.org/10.5302/J.ICROS.2013.13.8002
  7. D. J. Lee, Y. S. Hwang, Y. M. Yun, and J. M. Lee "2D Grid Map Compensation using an ICP Algorithm," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 11, pp. 1170-1174, 2014. https://doi.org/10.5302/J.ICROS.2014.14.8022
  8. S. B. Kim and H. B. Kim, "Comparative analysis on performance indices of obstacle detection for an overlapped ultrasonic sensor ring," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 18, no. 4, pp. 321-327, 2012. https://doi.org/10.5302/J.ICROS.2012.18.4.321
  9. T. Naito, I. Toshio, and K. Yukio, "The obstacle detection method using optical flow estimation at the edge image," IEEE Intelligent Vehicles Symposium, 2007.
  10. J. Zhou and L. Baoxin, "Robust ground plane detection with normalized homography in monocular sequences from a robot platform," IEEE International Conference on Image Processing, 2006.
  11. Y. Shen, D. Xin, and L. Jilin, "Monocular vision based obstacle detection for robot navigation in unstructured environment," Advances in Neural Networks 2007. Springer Berlin Heidelberg, pp. 714-722, 2007.
  12. S. Zehang, B. George, and M. Ronald, "Monocular precrash vehicle detection: Features and classifiers," IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 2019-2034, 2006. https://doi.org/10.1109/TIP.2006.877062
  13. H. A. Mallot, H. H. Bülthoff, J. J. Little, and S. Bohrer, "Inverse perspective mapping simplifies optical flow computation and obstacle detection," Biological cybernetics, vol. 64 no. 3, pp. 171-185, 1991.
  14. G. Ma, S. B. Park, S. Muller-Schneiders, A. Ioffe, and A. Kummert, "Vision-based pedestrian detection-reliable pedestrian candidate detection by combining IPM and a 1D profile," IEEE Intelligent Transportation Systems Conference, 2007.
  15. S. Kumar, D. Ayush, and K. K. Madhava, "A bayes filter based adaptive floor segmentation with homography and appearance cues," Indian Conference on Computer Vision, Graphics and Image Processing, 2012.
  16. X. N. Cui, Y. G. Kim, and H. I. Kim, "Floor segmentation by computing plane normals from image motion fields for visual navigation," International Journal of Control, Automation and Systems, vol. 7, no. 5, pp. 788-798, 2009. https://doi.org/10.1007/s12555-009-0511-2
  17. C. Wang and Z. K. Shi, "A novel traffic stream detection method based on inverse perspective mapping," Procedia Engineering, vol. 29, pp. 1938-1943, 2012. https://doi.org/10.1016/j.proeng.2012.01.240
  18. S. Tan, J. Dale, A. Anderson, and A. Johnston, "Inverse perspective mapping and optic flow: A calibration method and a quantitative analysis," Image and Vision Computing, vol. 24, no. 2, pp. 153-165, 2006. https://doi.org/10.1016/j.imavis.2005.09.023
  19. R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2nd edition, 2004.
  20. Y. Boykov, V. Olga, and Z. Ramin, "Fast approximate energy minimization via graph cuts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222-1239, 2001. https://doi.org/10.1109/34.969114