Skip to main content

Lightweight Laser Scan Registration in Underground Mines with Band-based Downsampling Method

  • Chapter
  • First Online:
Field and Service Robotics

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 92))

Abstract

Robots operating in underground mines must accurately track their location and create maps. The rough, undulating floors typical of mine environments preclude the 2D representation of scene integral to many existing real-time mobile robot simultaneous localization and mapping systems. On the other hand, a full 3D solution is made unrealistic by the computational expense of aligning large point clouds. This paper presents an approach that extracts high-density, horizontal bands of laser scans and uses them to represent the scene with detail sufficient to capture the moderate non-planar motion typical of mining robots. Our approach is able to operate in real-time, building maps and localizing in pace with range scanning and is fast enough to allow continuous vehicle motion. We present details of the approach which has been validated in an underground mine. Trials runs have shown a significant decrease in computation time without an appreciable decrease in accuracy over a full 3D strategy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. MRA, in Safety in Deep Mining conference http://www.spintelligent-events.com/deep-mining2008/en/index.php

  2. Bureau of Deep Mine Safety, Department of Environmental Protection, Commonwealth of Pennsylvania. Report of Investigation, Black Wolf Coal Company, Inc., Quecreek No. 1 Mine (2003)

    Google Scholar 

  3. J. Belwood, R. Waugh, Bats and mines: abandoned does not always mean empty. Bats 9(3), 13–16 (1991)

    Google Scholar 

  4. A. Morris, D. Ferguson, Z. Omohundro, D. Bradley, D. Silver, C. Baker, S. Thayer, C. Whittaker, W. Whittaker, Recent developments in subterranean robotics. J. Field Robot. 23(1), 35–57 (2006)

    Article  MATH  Google Scholar 

  5. M. Bosse, P. Newman, J. Leonard, S. Teller, Simultaneous localization and map building in large-scale cyclic environments using the atlas framework. Int. J. Robot. Res. 23(12), 1113–1139 (2004)

    Article  Google Scholar 

  6. M. Kaess, H. Johannsson, R. Roberts, V. Ila, J. Leonard, F. Dellaert, iSAM2: incremental smoothing and mapping with fluid relinearization and incremental variable reordering. ICRA 24(6), 1365–1378 (2011)

    Google Scholar 

  7. M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit, FastSLAM 2.0: An improved particle ltering algorithm for simultaneous localization and mapping that provably converges. Proc. Int. Jt. Conf. Artif. Intell. 18, 1151–1156 (2003)

    Google Scholar 

  8. S. Rusinkiewicz, M. Levoy. Effcient variants of the ICP algorithm. 3D Digital Imaging Model. 2001, 145–152 (2001)

    Google Scholar 

  9. N. Fairfield, G. Kantor, D. Wettergreen, Segmented SLAM in three-dimensional environments. J. Field Robot. 27(1), 85–103 (2010)

    Article  Google Scholar 

  10. M. Pauly, M. Gross, L. Kobbelt, Efficient simplification of point-sampled surfaces. IEEE Visual. 2(4), 163–170 (2002)

    Google Scholar 

  11. J. Bowers, R. Wang, L. Wei, D. Maletz. Parallel Poisson disk sampling with spectrum analysis on surfaces. ACM Trans. Graph. 29 166:1–166:10 (2010)

    Google Scholar 

  12. A. Torsello, E. Rodola, A. Albarelli. Sampling, Relevant Points for Surface Regis- tration. International Conference on 3D Imaging. Model. Process. Visual. Transm. 2011, 90–295 (2011)

    Google Scholar 

  13. J. Yao, M. Ruggeri, P. Taddei, V. Sequeira, Robust surface registration using N-points approximate congruent set. EURASIP J. Adv. Signal Process. 72, 1–22 (2011)

    Google Scholar 

  14. D. Cole, A. Harrison, P. Newman. Using naturally salient regions for SLAM with 3D laser data, in International Conference on Robotics and Automation SLAM, Workshop (2005)

    Google Scholar 

  15. A. Nuchter, H. Surmann, K. Lingemann, J. Hertzberg, S. Thrun, 6D SLAM with an application in autonomous mine mapping. ICRA 2, 1998–2003 (2004)

    Google Scholar 

  16. N. Gelfand, L. Ikemoto, S. Rusinkiewicz, M. Levoy. Geometrically stable sampling for the ICP algorithm. 3D Digital Imaging Model. 2003, 260–267 (2003)

    Google Scholar 

  17. E. Olson, J. Leonard, S. Teller, Fast iterative optimization of pose graphs with poor initial estimates. ICRA 2009, 2262–2269 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to James Lee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Lee, J., Wettergreen, D., Kantor, G. (2014). Lightweight Laser Scan Registration in Underground Mines with Band-based Downsampling Method. In: Yoshida, K., Tadokoro, S. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 92. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40686-7_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40686-7_37

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40685-0

  • Online ISBN: 978-3-642-40686-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics