FREE SPACE COMPUTATION FROM STOCHASTIC OCCUPANCY GRIDS BASED ON ICONIC KALMAN FILTERED DISPARITY MAPS

Carsten Høilund, Thomas B. Moeslund, Claus B. Madsen, Mohan M. Trivedi

Abstract

This paper presents a method for determining the free space in a scene as viewed by a vehicle-mounted camera. Using disparity maps from a stereo camera and known camera motion, the disparity maps are first filtered by an iconic Kalman filter, operating on each pixel individually, thereby reducing variance and increasing the density of the filtered disparity map. Then, a stochastic occupancy grid is calculated from the filtered disparity map, providing a top-down view of the scene where the uncertainty of disparity measurements are taken into account. These occupancy grids are segmented to indicate a maximum depth free of obstacles, enabling the marking of free space in the accompanying intensity image. The test shows successful marking of free space in the evaluated scenarios in addition to significant improvement in disparity map quality.

References

  1. Badino, H., Franke, U., and Mester, R. (2007). Free space computation using stochastic occupancy grids and dynamic programming. Workshop on Dynamical Vision, ICCV, Rio de Janeiro, Brazil.
  2. Elfes, A. (1989). Using occupancy grids for mobile robot perception and navigation. Computer, 22(6):46-57.
  3. Høilund, C. (2009). Free space computation from stochastic occupancy grids based on iconic Kalman filtered disparity maps. Master's thesis, Aalborg University, Laboratory of Computer Vision and Media Technology, Denmark.
  4. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASMEJournal of Basic Engineering, 82(Series D).
  5. Li, M., Hong, B., Cai, Z., Piao, S., and Huang, Q. (2008). Novel indoor mobile robot navigation using monocular vision. Engineering Applications of Artificial Intelligence, 21(3).
  6. Mahalanobis, P. C. (1936). On the generalised distance in statistics. In Proceedings National Institute of Science, India, volume 2.
  7. Matthies, L., Kanade, T., and Szeliski, R. (1989). Kalman filter-based algorithms for estimating depth from image sequences. International Journal of Computer Vision, 3(3):209-238.
  8. Murray, D. and Little, J. (2000). Using real-time stereo vision for mobile robot navigation. Autonomous Robots, 8(2).
  9. Trivedi, M. M., Gandhi, T., and McCall, J. (2007). Lookingin and looking-out of a vehicle: Computer-visionbased enhanced vehicle safety. Intelligent Transportation Systems, IEEE Transactions on, 8(1).
  10. TYZX (2009). TYZX DeepSea Stereo Camera. [Online; accessed 20-November-09]. http://www.tyzx.com/.
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Paper Citation


in Harvard Style

Høilund C., B. Moeslund T., B. Madsen C. and M. Trivedi M. (2010). FREE SPACE COMPUTATION FROM STOCHASTIC OCCUPANCY GRIDS BASED ON ICONIC KALMAN FILTERED DISPARITY MAPS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 164-167. DOI: 10.5220/0002830601640167


in Bibtex Style

@conference{visapp10,
author={Carsten Høilund and Thomas B. Moeslund and Claus B. Madsen and Mohan M. Trivedi},
title={FREE SPACE COMPUTATION FROM STOCHASTIC OCCUPANCY GRIDS BASED ON ICONIC KALMAN FILTERED DISPARITY MAPS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={164-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002830601640167},
isbn={978-989-674-028-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - FREE SPACE COMPUTATION FROM STOCHASTIC OCCUPANCY GRIDS BASED ON ICONIC KALMAN FILTERED DISPARITY MAPS
SN - 978-989-674-028-3
AU - Høilund C.
AU - B. Moeslund T.
AU - B. Madsen C.
AU - M. Trivedi M.
PY - 2010
SP - 164
EP - 167
DO - 10.5220/0002830601640167