Skip to main content
SpringerLink
Log in

Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Almqvist, H., Magnusson, M., Stoyanov, T., Lilienthal, A.J.: Improving point-cloud accuracy from a moving platform in field operations. In: Proc. IEEE/RSJ Int. Conf on Robotics and Automation, pp. 725–730 (2013)

  2. Altafini, C.: A path tracking criterion for an LHD articulated vehicle. Int. J. Robot. Res. 19(5), 435–441 (1999)

    Google Scholar 

  3. Andreasson, H., Magnusson, M., Lilienthal, A.J.: Has something change here? Autonomous difference detection for security patrol robots. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), San Diego, USA, pp. 3429–3435 (2007)

  4. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  5. Biber, P., Fleck, S., Straßer, W.: A probabilistic framework for robust and accurate matching of point clouds. In: 26th Pattern Recognition Symposium (DAGM 04) (2004)

  6. Biber, P., Straßer, W.: The normal distributions transform: a new approach to laser scan matching. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, pp. 2743–2748 (2003)

  7. Bohren, J., Foote, T., Keller, J., Kushleyev, A., Lee, D., Stewart, A., Vernaza, P., Derenick, J., Spletzer, J., Satterfield, B.: Little ben: the ben franklin racing team’s entry in the 2007 DARPA urban challenge. J. Field Robot. 25, 598–614 (2008). doi:10.1002/rob.20260

    Article  Google Scholar 

  8. Bosse, M., Zlot, R.: Continuous 3D scan-matching with a spinning 2D laser. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, pp. 4312–4319 (2009)

  9. Bosse, M., Zlot, R., Flick, P.: Zebedee: design of a spring-mounted 3D range sensor with application to mobile mapping. IEEE Trans. Robot. Robot. 28(5), 1104–1119 (2012)

    Article  Google Scholar 

  10. Burguera, A., González, Y., Oliver, G.: The likelihood field approach to sonar scan matching. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), Nice, France, pp. 2977–2982 (2008)

  11. Burguera, A., González, Y., Oliver, G.: On the use of likelihood fields to perform sonar scan matching localization. Auton. Robot. 26(4), 203–222 (2009)

    Article  Google Scholar 

  12. Frese, U.: A discussion of simultaneous localization and mapping. Auton. Robot. 20(1), 25–42 (2006)

    Article  Google Scholar 

  13. Grisetti, G., Stachniss, C., Burgard, W.: Non-linear constraint network optimization for efficient map learning. IEEE Trans. Intell. Transp. Syst. 10(3), 428–439 (2009)

    Article  Google Scholar 

  14. Harrison, A., Newman, P.: High quality 3D laser ranging under general vehicle motion. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, USA, pp. 7–12 (2008)

  15. Klasing, K., Wollherr, D., Buss, M.: A clustering method for efficient segmentation of 3D laser data. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, USA, pp. 4043–4048 (2008)

  16. Koyachi, N., Sarata, S.: Unmanned loading operation by autonomous wheel loader. In: Proceedings of the ICROS-SICE International Joint Conference, pp. 2221–2225 (2009)

  17. Magnusson, M.: The three-dimensional normal-distributions transform—an efficient representation for registration, Surface Analysis, and Loop Detection. Ph.D. thesis, Örebro University, Sweden. Örebro Studies in Technology 36 (2009)

  18. Magnusson, M., Almqvist, H.: Consistent pile-shape quantification for autonomous wheel loaders. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 4078–4083 (2011)

  19. Magnusson, M., Duckett, T.: A comparison of 3D registration algorithms for autonomous underground mining vehicles. In: Proceedings of the European Conference on Mobile Robots (ECMR), Ancona, Italy, pp. 86–91 (2005)

  20. Magnusson, M., Lilienthal, A.J., Duckett, T.: Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robot. 24(10), 803–827 (2007)

    Article  Google Scholar 

  21. Magnusson, M., Nüchter, A., Lörken, C., Lilienthal, A.J., Hertzberg, J.: Evaluation of 3D registration reliability and speed—a comparison of ICP and NDT. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, pp. 3907–3912 (2009)

  22. McIvor, A.M., Valkenburg, R.J.: A comparison of local surface geometry estimation methods. Mach. Vis. Appl. 10, 17–26 (1997)

    Article  Google Scholar 

  23. Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6D SLAM—3D mapping outdoor environments. J. Field Robot. 24(8–9), 699–722 (2007)

    Article  MATH  Google Scholar 

  24. Petitjean, S.: A survey of methods for recovering quadrics in triangle meshes. ACM Comput. Surv. 34(2), 211–262 (2002)

    Article  Google Scholar 

  25. Sarata, S.: Model-based task planning for loading operation in mining. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), Maui, USA, pp. 439–445 (2001)

  26. Sarata, S., Koyachi, N., Sugawara, K.: Field test of autonomous loading operation by wheel loader. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), Nice, France, pp. 2661–2666 (2008)

  27. Sarata, S., Koyachi, N., Tsubouchi, T., Osumi, H., Kurisu, M., Sugawara, K.: Development of autonomous system for loading operation by wheel loader. In: Proceedings of the International Symposium on Automation and Robotics in Construction, pp. 466–471 (2006)

  28. Sarata, S., Osumu, H., Kaway, Y., Tomita, F.: Trajectory arrangement based on resistance force and shape of pile at scooping motion. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), New Orleans, USA, pp. 3488–3493 (2004)

  29. Sarata, S., Weerakamhaeng, Y., Tsubouchi, T.: Approach path generation to scooping position for wheel loader. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, pp. 1809–1814 (2005)

  30. Sarata, S., Weeramhaeng, Y., Tsubouchi, T.: Planning of scooping position and approach path for loading operation by wheel loader. In: Proceedings of the International Symposium on Automation and Robotics in Construction, Ferrara, Italy (2005)

  31. Singh, S., Cannon, H.: Multi-resolution planning for earthmoving. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Leuven, Belgium (1998)

  32. Stoyanov, T., Lilienthal, A.J.: Maximum likelihood point cloud acquisition from a rotating laser scanner on a moving platform. In: Proceedings of the International Conference on Advanced Robotics (2009)

  33. Stoyanov, T., Louloudi, A., Andreasson, H., Lilienthal, A.J.: Comparative evaluation of range sensor accuracy in indoor environments. In: Proceedings of the European Conference on Mobile Robots (ECMR) (2011)

  34. Stoyanov, T., Magnusson, M., Andreasson, H., Lilienthal, A.J.: Path planning in 3d environments using the normal distributions transform. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS) (2010)

  35. Takeuchi, E., Tsubouchi, T.: A 3-D scan matching using improved 3-D normal distributions transform for mobile robotic mapping. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), Beijing, China, pp. 3068–3073 (2006)

  36. Torr, P.H.S., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comp. Vision Image Underst. 78(1):138–156 (2000)

    Article  Google Scholar 

  37. Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M.N., Dolan, J., Duggins, D., Galatali, T., Geyer, C., Gittleman, M., Harbaugh, S., Hebert, M., Howard, T.M., Kolski, S., Kelly, A., Likhachev, M., McNaughton, M., Miller, N., Peterson, K., Pilnick, B., Rajkumar, R., Rybski, P., Salesky, B., Seo, Y.-W., Singh, S., Snider, J., Stentz, A., Whittaker, W., Wolkowicki, Z., Ziglar, J., Bae, H., Brown, T., Demitrish, D., Litkouhi, B., Nickolaou, J., Sadekar, V., Zhang, W., Struble, J., Taylor, M., Darms, M. and Ferguson, D.: Autonomous driving in urban environments: boss and the urban challenge. J. Field Robot. 25, 425–466 (2008). doi:10.1002/rob.20255

    Article  Google Scholar 

  38. Wulf, O., Wagner, B.: Fast 3D-scanning methods for laser measurement systems. In: International Conference on Control Systems and Computer Science (CSCS14) (2003)

  39. Zlot, R., Bosse, M.: Efficient large-scale 3d mobile mapping and surface reconstruction of an underground mine. In: International Conference on Field and Service Robotics (2012)

Download references

Author information

Authors and Affiliations

  1. Center for Applied Autonomous Sensor Systems (AASS), Örebro University, Orebro, Sweden

    Håkan Almqvist, Martin Magnusson & Achim J. Lilienthal

Authors
  1. Håkan Almqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Magnusson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Achim J. Lilienthal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Håkan Almqvist.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Almqvist, H., Magnusson, M. & Lilienthal, A.J. Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation. J Intell Robot Syst 75, 101–128 (2014). https://doi.org/10.1007/s10846-013-9957-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-013-9957-9

Keywords

Mathematics Subject Classification (2010)

Search

Navigation