Advertisement

Stereo Vision-Based Optimal Path Planning with Stochastic Maps for Mobile Robot Navigation

  • Taimoor Shakeel Sheikh
  • Ilya M. Afanasyev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

This paper addresses the problem of stereo vision-based environment mapping and optimal path planning for an autonomous mobile robot by using the methodology of getting 3D point cloud from disparity images, its transformation to 2D stochastic navigation map with occupancy grid cell values assigned from the set {obstacle, unoccupied, occupied}. We re-examined and extended this methodology with a combination of A-Star with binary heap algorithms for obstacle avoidance and indoor navigation of the Innopolis autonomous mobile robot with ZED stereo camera.

Keywords

Stereo vision Optimal path planning Stochastic map Occupancy grid Disparity image Mobile robot navigation 

Notes

Acknowledgement

This work has been supported by the Russian Ministry of education and science with the project “Development of anthropomorphic robotic complexes with variable stiffness actuators for movement on the flat and the rugged terrains” (agreement: No14.606.21.0007, ID: RFMEFI60617X0007).

References

  1. 1.
    Agha-Mohammadi, A.A.: Smap: Simultaneous mapping and planning on occupancy grids. arXiv preprint arXiv:1608.04712 (2016)
  2. 2.
    Andert, F.: Drawing stereo disparity images into occupancy grids: measurement model and fast implementation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5191–5197. IEEE (2009)Google Scholar
  3. 3.
    Broggi, A., Cattani, S., Patander, M., Sabbatelli, M., Zani, P.: A full-3d voxel-based dynamic obstacle detection for urban scenario using stereo vision. In: IEEE Intelligent Transportation Systems Conference (ITSC), pp. 71–76. IEEE (2013)Google Scholar
  4. 4.
    Burschkal, D., Lee, S., Hager, G.: Stereo-based obstacle avoidance in indoor environments with active sensor re-calibration. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 2, pp. 2066–2072. IEEE (2002)Google Scholar
  5. 5.
    Elinas, P., Sim, R., Little, J.J.: /spl sigma/slam: stereo vision slam using the rao-blackwellised particle filter and a novel mixture proposal distribution. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1564–1570. IEEE (2006)Google Scholar
  6. 6.
    Hirschmuller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 807–814. IEEE (2005)Google Scholar
  7. 7.
    Hrabar, S.: 3d path planning and stereo-based obstacle avoidance for rotorcraft uavs. In: International Conference on Intelligent Robots and Systems (IROS), pp. 807–814. IEEE (2008)Google Scholar
  8. 8.
    Ibragimov, I.Z., Afanasyev, I.M.: Comparison of ros-based visual slam methods in homogeneous indoor environment. In: 14th Workshop on Positioning, Navigation and Communications (WPNC), pp. 1–6. IEEE (2017)Google Scholar
  9. 9.
    Konolige, K.: Small vision systems: Hardware and implementation. In: Robotics Research, pp. 203–212. Springer (1998)Google Scholar
  10. 10.
    Kumar, S.: Binocular stereo vision based obstacle avoidance algorithm for autonomous mobile robots. In: IEEE International Advance Computing Conference (IACC), pp. 254–259. IEEE (2009)Google Scholar
  11. 11.
    Lategahn, H., Graf, T., Hasberg, C., Kitt, B., Effertz, J.: Mapping in dynamic environments using stereo vision. In: IEEE Intelligent Vehicles Symposium (IV), pp. 150–156. IEEE (2011)Google Scholar
  12. 12.
    Li, Y., Ruichek, Y.: Occupancy grid mapping in urban environments from a moving on-board stereo-vision system. Sensors 14(6), 10454–10478 (2014)CrossRefGoogle Scholar
  13. 13.
    Ma, H.: Research on interactive segmentation algorithm based on search path optimization. In: International Conference on Intelligent Human Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 286–289. IEEE (2009)Google Scholar
  14. 14.
    Matthies, L., Brockers, R., Kuwata, Y., Weiss, S.: Stereo vision-based obstacle avoidance for micro air vehicles using disparity space. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3242–3249. IEEE (2014)Google Scholar
  15. 15.
    Schmid, K., Tomic, T., Ruess, F., Hirschmüller, H., Suppa, M.: Stereo vision based indoor/outdoor navigation for flying robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3955–3962. IEEE (2013)Google Scholar
  16. 16.
    Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. Int. J. Robot. Res. 21(8), 735–758 (2002)CrossRefGoogle Scholar
  17. 17.
    Shimchik, I., Sagitov, A., Afanasyev, I., Matsuno, F., Magid, E.: Golf cart prototype development and navigation simulation using ros and gazebo. In: MATEC Web of Conferences, vol. 75, p. 09005. EDP Sciences (2016)Google Scholar
  18. 18.
    Siegwart, R., Nourbakhsh, I.R., Scaramuzza, D.: Introduction to Autonomous Mobile Robots. MIT Press, Cambridge (2011)Google Scholar
  19. 19.
    Thrun, S., et al.: Robotic mapping: a survey. Explor. Artif. Intell. New Millenn. 1, 1–35 (2002)Google Scholar
  20. 20.
    Yousif, K., Bab-Hadiashar, A., Hoseinnezhad, R.: An overview to visual odometry and visual slam: applications to mobile robotics. Intell. Ind. Syst. 1(4), 289–311 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of RoboticsInnopolis UniversityInnopolisRussia

Personalised recommendations