Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F40

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 669–674 (2017)

Full text

Abstract. In this paper we propose a fast method for detecting the ground plane in 3D scenes for an arbitrary roll angle rotation of a stereo vision camera. The method is based on the analysis of the disparity map and its``V-disparity'' representation. First, the roll angle of the camera is identified from the disparity map. Then, the image is rotated to a zero-roll angle position and the ground plane is detected from the V-disparity map. The proposed method was successfully verified on a simulated 3D scene image sequences as well as on the recorded outdoor stereo video sequences. The foreseen application of the method is the sensory substitution assistive device aiding the visually impaired in the space perception and mobility.

References

1. Y. Cong, J. J. Peng, J. Sun, L. L. Zhu and Y. D Tang, “V-disparity based UGV obstacle detection in rough outdoor terrain,” Acta Automatica Sinica, vol. 36 (5), 2010, pp. 667–673, http://dx.doi.org10.1016/ S1874-1029(09)60029-X
2. Y. Li and Y. Ruichek, “Occupancy grid mapping in urban environments from a moving on-board stereo vision system,” Sensors, vol. 14, 2014, pp. 10454–10478, http://dx.doi.org/10.1016/S1874-1029(09)60029-X
3. M. Wu, S. K. Lam and T. Srikanthan, “Nonparametric Technique Based High-Speed Road Surface Detection,” in IEEE Transactions on Intelligent Transportation Systems, vol. 16 (2), 2015, pp. 874–884, http://dx.doi.org/10.1109/TITS.2014.2345413
4. C. Yu, V. Cherfaoui and P. Bonnifait, “Evidential occupancy grid mapping with stereo vision,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), June 2015, pp. 712–717, http://dx.doi.org/10.1109/IVS.2015.7225768
5. D. Yiruo, W. Wenjia and K. Yukihiro, “Complex ground plane detection based on V-disparity map in off-road environment,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), June 2013, pp. 1137–1142, http://dx.doi.org/10.1109/IVS.2013.6629619
6. A. Iloie, I. Giosan and S. Nedevschi, “UV disparity based obstacle detection and pedestrian classification in urban traffic scenarios,” in Proceedings of the IEEE Int Intelligent Computer Communication and Processing (ICCP) Conference, September 2014, pp. 119–125, http://dx.doi.org/10.1109/ICCP.2014.6936963
7. X. Zhu, H. Lu, X. Yang, Y. Li and H. Zhang, “Stereo vision based traversable region detection for mobile robots using u-v-disparity,” in Proc. 32nd Chinese Control Conference (CCC), July 2013, pp. 5785–5790.
8. T. S. Leung and G. Medioni, “Visual Navigation Aid for the Blind in Dynamic Environments,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, 2014, pp. 579-586. http://dx.doi.org/10.1109/CVPRW.2014.89
9. Y. Lin, F. Guo and S. Li, “Road Obstacle Detection in Stereo Vision Based on UV-disparity,” Journal of Information & Computational Science, vol. 11 (4), 2014, pp. 1137–1144, http://dx.doi.org/10.12733/ jics20103012
10. Z. Hu, F. Lamosa and K. Uchimura, “A complete U-V-disparity study for stereovision based 3D driving environment analysis,” in Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM’05), 2005, pp. 204-211, http://dx.doi.org/10.1109/3DIM.2005.6
11. J. Suhr, H. Kang and H. Jung, “Dense stereo-based critical area detection for active pedestrian protection system,” Electronic Letters, vol. 48 (19), 2012, pp. 1199–1201, http://dx.doi.org/10.1049/el.2012.1176
12. M. Owczarek, P. Skulimowski and P. Strumillo, “Sound of Vision – 3D Scene Reconstruction from Stereo Vision in an Electronic Travel Aid for the Visually Impaired,” in: Computers Helping People with Special Needs, ICCHP 2016, Lecture Notes in Computer Science, vol. 9759, pp. 35–42, 2016, http://dx.doi.org/10.1007/978-3-319-41267-2_6
13. P. Skulimowski and P. Strumillo, “Verification of visual odometry algorithms with an OpenGL-based software tool,” Journal of Electronic Imaging, vol. 24 (3), 2015, pp. 033003, http://dx.doi.org/10.1117/1.JEI. 24.3.033003
14. R. Labayrade, D. Aubert and J. P. Tarel, “Real Time Obstacle Detection in stereo vision on Non Flat Road Geometry Through “V-disparity” Representation,” in Proceedings of the IEEE Intelligent Vehicles Symposium, 2002, pp. 646–651, http://dx.doi.org/10.1109/IVS.2002.1188024
15. M. Z. Brown, D. Burschka, and G. D. Hager, “Advances in computational stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25 (8), August 2003, pp. 993–1008, http://dx.doi.org/10.1109/TPAMI.2003.1217603
16. D. A. Forsyth and J. Ponce, Computer Vision: A Modern Approach, Pearson Education, Inc., 2003.
17. R. Labayrade and D. Aubert, “A single framework for vehicle roll, pitch, yaw estimation and obstacles detection by stereo vision,” in Proceedings of the IEEE Intelligent Vehicles Symposium, 2003, pp. 31–36, http://dx.doi.org/10.1109/IVS.2003.1212878
18. “Developer Center - ZED,” 2017, visited on 2017-04-28. [Online]. Available: https://www.stereolabs.com/developers/.
19. P. Skulimowski, “UV-disparity analysis: Ground plane estimation results for simulated and real outdoor scenes,” 2017, visited on 2017-04-28. [Online]. Available: http://uv-disparity.naviton.pl/.