Autonomous Robot Navigation: Path Planning on a Detail-Preserving Reduced-Complexity Representation of 3D Point Clouds

  • Rohit Sant
  • Ninad Kulkarni
  • Ainesh Bakshi
  • Salil Kapur
  • Kratarth Goel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7963)


Determination of a collision free, optimal path achieved by performing complex computations on an accurate representation of the terrain, is essential to the success of autonomous navigation systems. This paper defines and builds upon a technique – Spatially Important Point (SIP) Identification – for reducing the inherent complexity of range data without discarding any essential information. The SIP representation makes onboard autonomous navigation a viable option, since space and time complexity is greatly reduced. A cost based, dynamic navigation analysis is then performed using only the SIPs which culminates into a new time and space efficient Path Planning technique. The terrain is also retained in the form of a graph, with each branch of every node encountered, indexed in a priority sequence given by its cumulative cost. Experiments show the entire dataflow, from input data to path planning, being done in less than 700ms on a modern computer on datasets typically having 106 points.


Autonomous navigation path planning range data dynamic programming spatially important points 


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  1. 1.
    Sant, R., Goel, K., Kulkarni, N., Bakshi, A., Kapur, S.: Spatially Important Point Identification: A New Technique for Detail-Preserving Reduced-Complexity Representation of 3D Point Clouds (March 22, 2013), Retrieved from Google Drive,
  2. 2.
    Zhao, Y., He, M., Zhao, H., Davoine, F., Zha, H.: Computing Object-based Saliency in Urban Scenes Using Laser Sensing. In: International Conference on Robotics and Automation, pp. 4436–4443. IEEE, Saint Paul (2012)Google Scholar
  3. 3.
    Swadzba, A., Vollmer, A., Hanheide, M., Wachsmuth, S.: Reducing noise and redundancy in registered range data for planar surface extraction. In: 19th International Conference on Pattern Recognition, pp. 1–4. IEEE, Tampa (2008)Google Scholar
  4. 4.
    Moenning, C., Dodgson, N.A.: A New Point Cloud Simplification Algorithm. In: Proceedings of 3rd IASTED Conference on Visualization, Imaging and Image Processing, pp. 1027–1033. IASTED, Benalmádena (2003)Google Scholar
  5. 5.
    Tong, C., Gingras, D., Larose, K., Barfoot, T.D., Dupuis, E.: The Canadian Planetary Emulation Terrain 3D Mapping Dataset. International Journal of Robotics Research (IJRR) (2012)Google Scholar
  6. 6.
    Lee, K.H., Woo, H., Suk, T.: Data Reduction Methods for Reverse Engineering. Int. J. Adv. Manuf. Technol., 735–743 (2001)Google Scholar
  7. 7.
    Song, W., Cai, S., Yang, B., Cui, W., Wang, Y.: A Reduction Method of Three-Dimensional Point Cloud. In: 2nd International Conference on Biomedical Engineering and Informatics, pp. 1–4. IEEE, Tianjin (2009)Google Scholar
  8. 8.
    Brooks, R.A., Lorenzo-Perez, T.: A subdivision algorithm in configuration space for findpath with rotation. IEEE Transactions on Systems, Man and Cybernetics SMC-15(2), 225–233 (1985)Google Scholar
  9. 9.
    Jensen, R.M., Bryant, R.E., Veloso, M.M.: SetA*: An efficient BDD-based heuristic search algorithm. Tech. rep., Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA (August 2002)Google Scholar
  10. 10.
    Beginners guide to pathfinding algorithms, (visited on September 3, 2006)
  11. 11.
    Nashashibi, F., Fillatreau, P., Dacre-Wright, B., Simeon, T.: 3-D autonomous navigation in a natural environment. In: Proceedings of the 1994 IEEE International Conference on Robotics and Automation, May 8-13, vol. 1, pp. 433–439 (1994)Google Scholar
  12. 12.
    Moorthy, I., Millert, J.R., Hut, B., Berni, J.A., Zareo-Tejada, P.J., Lit, Q.: Extracting tree crown properties from ground-based scanning laser data. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 2830–2832. IEEE, Barcelona (2007)Google Scholar
  13. 13.
    Zhao, H., Liu, Y., Zhu, X., Zhao, Y., Zha, H.: Scene Understanding in a Large Dynamic Environment through Laser-Based Sensing. In: International Conference on Robotics and Automation, pp. 127–133. IEEE, Anchorage (2010)Google Scholar
  14. 14.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: 2nd ACM International Conference on Knowledge Discovery and Data Mining (KDD), pp. 226–231. ACM, Portland (1996)Google Scholar
  15. 15.
    Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–4. IEEE, Shanghai (2011)CrossRefGoogle Scholar
  16. 16.
    Takahashi, O., Schilling, J.: Motion planning in a plane using generalized Voronoi diagrams. IEEE Transactions on Robotics and Automation 5(2), 143–150 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rohit Sant
    • 1
  • Ninad Kulkarni
    • 1
  • Ainesh Bakshi
    • 1
  • Salil Kapur
    • 1
  • Kratarth Goel
    • 1
  1. 1.BITS-PilaniGoaIndia

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