Approximate Distance Queries for Path-planning in Massive Point Clouds

David Eriksson, Evan Shellshear

2014

Abstract

In this paper, algorithms have been developed that are capable of efficiently pre-processing massive point clouds for the rapid computation of the shortest distance between a point cloud and other objects (e.g. triangulated, point-based, etc.). This is achieved by exploiting fast distance computations between specially structured subsets of a simplified point cloud and the other object. This approach works for massive point clouds even with a small amount of RAM and was able to speed up the computations, on average, by almost two orders of magnitude. Given only 8 GB of RAM, this resulted in shortest distance computations of 30 frames per second for a point cloud originally having 1 billion points. The findings and implementations will have a direct impact for the many companies that want to perform path-planning applications through massive point clouds since the algorithms are able to produce real-time distance computations on a standard PC.

References

  1. Berlin, R. (2002). Accurate robot and workcell simulation based on 3d laser scanning. In Proceedings of the 33rd ISR (International Symposium on Robotics).
  2. Bialkowski, J., Karaman, S., Otte, M., and Frazzoli, E. (2013). Efficient collision checking in samplingbased motion planning. In Algorithmic Foundations of Robotics X, pages 365-380. Springer.
  3. Carlson, J. S., Spensieri, D., S öderberg, R., Bohlin, R., and Lindkvist, L. (2013). Non-nominal path planning for robust robotic assembly. Journal of manufacturing systems, 32(3):429-435.
  4. Dementiev, R., Kettner, L., and Sanders, P. (2008). Stxxl: standard template library for xxl data sets. Software: Practice and Experience, 38(6):589-637.
  5. Dupuis, E., Rekleitis, I., Bedwani, J.-L., Lamarche, T., Allard, P., and Zhu, W.-H. (2008). Over-the-horizon autonomous rover navigation: experimental results. In International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), Los Angeles, CA.
  6. Eriksson, D. (2014). Point cloud simplification and processing for path-planning. Master's thesis, Chalmers University of Technology.
  7. Fröhlich, C. and Mettenleiter, M. (2004). Terrestrial laser scanning-new perspectives in 3d surveying. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(Part 8):W2.
  8. Hermansson, T., Bohlin, R., Carlson, J. S., and S öderberg, R. (2012). Automatic path planning for wiring harness installations (wt). In 4th CIRP Conference on Assembly Technology and Systems-CATS 2012, University of Michigan, Ann Arbor, USA on May 21-23, 2012.
  9. Landa, Y. (2008). Visibility of point clouds and exploratory path planning in unknown environments. ProQuest.
  10. Landa, Y., Galkowski, D., Huang, Y. R., Joshi, A., Lee, C., Leung, K. K., Malla, G., Treanor, J., Voroninski, V., Bertozzi, A. L., et al. (2007). Robotic path planning and visibility with limited sensor data. In American Control Conference, 2007. ACC'07, pages 5425- 5430. IEEE.
  11. Larsen, E., Gottschalk, S., Lin, M. C., and Manocha, D. (1999). Fast proximity queries with swept sphere volumes. Technical report, Technical Report TR99-018, Department of Computer Science, University of North Carolina.
  12. Latombe, J.-C. (1990). ROBOT MOTION PLANNING.: Edition en anglais. Springer.
  13. Lauterbach, C., Mo, Q., and Manocha, D. (2010). gproximity: Hierarchical gpu-based operations for collision and distance queries. In Computer Graphics Forum, volume 29, pages 419-428. Wiley Online Library.
  14. LaValle, S. M. (2006). Planning algorithms. Cambridge University Press.
  15. Lloyd, S. (1982). Least squares quantization in PCM. Information Theory, IEEE Transactions on, 28(2):129- 137.
  16. Pan, J., Chitta, S., and Manocha, D. (2011). Probabilistic collision detection between noisy point clouds using robust classification. In International Symposium on Robotics Research.
  17. Pauly, M., Gross, M., and Kobbelt, L. P. (2002). Efficient simplification of point-sampled surfaces. In Visualization, 2002. VIS 2002. IEEE, pages 163-170. IEEE.
  18. Sankaranarayanan, J., Samet, H., and Varshney, A. (2007). A fast all nearest neighbor algorithm for applications involving large point-clouds. Computers & Graphics, 31(2):157-174.
  19. Spensieri, D., Bohlin, R., and Carlson, J. S. (2013). Coordination of robot paths for cycle time minimization. In CASE, pages 522-527.
  20. Spensieri, D., Carlson, J. S., Bohlin, R., and S öderberg, R. (2008). Integrating assembly design, sequence optimization, and advanced path planning. ASME Conference Proceedings, (43253):73-81.
  21. Sucan, I. A., Kalakrishnan, M., and Chitta, S. (2010). Combining planning techniques for manipulation using realtime perception. In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages 2895-2901. IEEE.
  22. Tafuri, S., Shellshear, E., Bohlin, R., and Carlson, J. S. (2012). Automatic collision free path planning in hybrid triangle and point models: a case study. In Proceedings of the Winter Simulation Conference, page 282. Winter Simulation Conference.
  23. Volvo (2013). Volvo Cars in Gothenburg, Personal communication.
  24. Yoon, S.-E., Salomon, B., Lin, M., and Manocha, D. (2004). Fast collision detection between massive models using dynamic simplification. In Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, pages 136-146. ACM.
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Paper Citation


in Harvard Style

Eriksson D. and Shellshear E. (2014). Approximate Distance Queries for Path-planning in Massive Point Clouds . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-040-6, pages 20-28. DOI: 10.5220/0005002000200028


in Bibtex Style

@conference{icinco14,
author={David Eriksson and Evan Shellshear},
title={Approximate Distance Queries for Path-planning in Massive Point Clouds},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2014},
pages={20-28},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005002000200028},
isbn={978-989-758-040-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Approximate Distance Queries for Path-planning in Massive Point Clouds
SN - 978-989-758-040-6
AU - Eriksson D.
AU - Shellshear E.
PY - 2014
SP - 20
EP - 28
DO - 10.5220/0005002000200028