Shader-based Automatic Camera Layout Optimization for Mobile Robots using Genetic Algorithm

Shuiying Wang, Raúl Rojas

2014

Abstract

Given a mobile robot and a certain number of cameras, this paper addresses the problem of finding locations and orientations of the cameras relative to the robot such that an optimality criteria is maximized. The optimality criteria designed in this paper emphasizes the trade-off between the coverage of area of interest around the robot by the cameras subject to occlusion constraints and the proximity of cameras to the robot structure. Real coded genetic algorithm is employed to search for such optimal layout and the optimality criteria serves as the fitness function. The computation intensive parts, namely the coverage and proximity analysis, are adapted to such a form that GPU with programmable shader can be accommodated to accelerate them. A graphical user interface tool is constructed to allow observation and checks during the optimization process. Promising results are displayed in an experiment concerning a truck with seven cameras. The optimization framework outlined in this paper can also be extended to optimize layout of scanning sensors like LiDAR and Radar mounted on arbitrary structures.

References

  1. Becker, E., Guerra-Filho, G., and Makedon, F. (2009). Automatic sensor placement in a 3d volume. In Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments, page 36. ACM.
  2. David, P., Idasiak, V., and Kratz, F. (2007). A sensor placement approach for the monitoring of indoor scenes. Smart Sensing and Context, pages 110-125.
  3. Erdem, U. and Sclaroff, S. (2006). Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements. Computer Vision and Image Understanding, 103(3):156-169.
  4. Fleishman, S., Cohen-Or, D., and Lischinski, D. (2000). Automatic camera placement for image-based modeling. In Computer Graphics Forum, volume 19, pages 101-110. Wiley Online Library.
  5. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning.
  6. Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1):66-72.
  7. Jourdan, D. and de Weck, O. (2004). Multi-objective genetic algorithm for the automated planning of a wireless sensor network to monitor a critical facility. In Proceedings of the SPIE Defense and Security Symposium, volume 5403, pages 565-575.
  8. Matsumoto, M. and Nishimura, T. (1998). Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation (TOMACS), 8(1):3-30.
  9. Murray, A., Kim, K., Davis, J., Machiraju, R., and Parent, R. (2007). Coverage optimization to support security monitoring. Computers, Environment and Urban Systems, 31(2):133-147.
  10. O'Rourke, J. (1987). Art gallery theorems and algorithms, volume 57. Oxford University Press Oxford.
  11. Sharapov, R. R. and Lapshin, A. V. (2006). Convergence of genetic algorithms. Pattern Recognition and Image Analysis, 16(3):392-397.
  12. Sivaraj, R. and Ravichandran, T. (2011). A review of selection methods in genetic algorithm. International Journal of Engineering Science and Technology, 3(5):3792-3797.
  13. Wang, S., Heinrich, S., Wang, M., and Rojas, R. (2012). Shader-based sensor simulation for autonomous car testing. In Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, pages 224-229. IEEE.
  14. Wright, A. H. et al. (1991). Genetic algorithms for real parameter optimization. Foundations of genetic algorithms, 1:205-218.
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Paper Citation


in Harvard Style

Wang S. and Rojas R. (2014). Shader-based Automatic Camera Layout Optimization for Mobile Robots using Genetic Algorithm . In Proceedings of the 9th International Conference on Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2014) ISBN 978-989-758-002-4, pages 153-160. DOI: 10.5220/0004692201530160


in Bibtex Style

@conference{grapp14,
author={Shuiying Wang and Raúl Rojas},
title={Shader-based Automatic Camera Layout Optimization for Mobile Robots using Genetic Algorithm},
booktitle={Proceedings of the 9th International Conference on Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2014)},
year={2014},
pages={153-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004692201530160},
isbn={978-989-758-002-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2014)
TI - Shader-based Automatic Camera Layout Optimization for Mobile Robots using Genetic Algorithm
SN - 978-989-758-002-4
AU - Wang S.
AU - Rojas R.
PY - 2014
SP - 153
EP - 160
DO - 10.5220/0004692201530160