Graph Structure-Based Simultaneous Localization and Mapping with Iterative Closest Point Constraints in Uneven Outdoor Terrain

  • Taekjun Oh
  • Hyongjin Kim
  • Donghwa Lee
  • Hyun Chul Roh
  • Hyun Myung
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 345)

Abstract

The purpose of this study is to propose a novel mobile robot localization method applicable to outdoor environments, such as an uneven terrain. In order to solve the robot localization problem, we exploit state of the art graph-based SLAM (Simultaneous Localization and Mapping) algorithm and ICP (Iterative Closest Point) algorithm considering the gyroscopic data as a constraint for a graph structure. We confirm our method by testing actual sensor data acquired from a vehicle in outdoor environments and show that our proposed method is improved and suitable for uneven terrain.

Keywords

SLAM Graph Structure ICP Point Cloud UGV 

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References

  1. 1.
    Smith, R.C., Peter, C.: On the Representation and Estimation of Spatial Uncertainty. The International Journal of Robotics Research 5(4), 56–68 (1986)CrossRefGoogle Scholar
  2. 2.
    Nistér, D., Oleg, N., James, B.: Visual Odometry for Ground Vehicle Applications. Journal of Field Robotics 23(1), 3–20 (2006)CrossRefMATHGoogle Scholar
  3. 3.
    Zhang, Z.: Iterative Point Matching for Registration of Free-form Curves and Surfaces. International Journal of Computer Vision 13(2), 119–152 (1994)CrossRefGoogle Scholar
  4. 4.
    Obst, M., Bauer, S., Reisdorf, P., Wanielik, G.: Multipath Detection with 3D Digital Maps for Robust Multi-constellation GNSS/INS Vehicle Localization in Urban Areas. In: IEEE Intelligent Vehicles Symposium, pp. 184–190. IEEE Press, New York (2012)Google Scholar
  5. 5.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. In: AAAI/IAAI, pp. 593–598 (2002)Google Scholar
  6. 6.
    Grisetti, G., Stachniss, C., Burgard, W.: Improving Grid-based SLAM with Rao-blackwellized Particle Filters by Adaptive Proposals and Selective Resampling. In: IEEE International Conference on Robotics and Automation, pp. 2432–2437. IEEE Press, New York (2005)Google Scholar
  7. 7.
    Dellaert, F., Kaess, M.: Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing. The International Journal of Robotics Research 25(12), 1181–1203 (2006)CrossRefMATHGoogle Scholar
  8. 8.
    Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: iSAM2: Incremental Smoothing and Mapping using the Bayes Tree. The International Journal of Robotics Research 31(2), 217–236 (2011)Google Scholar
  9. 9.
    Kim, H., Oh, T., Lee, D., Choe, Y., Chung, M.J., Myung, H.: Mobile Robot Localization by Matching 2D Image Features to 3D Point Cloud. In: Ubiquitous Robots and Ambient Intelligence (2013)Google Scholar
  10. 10.
    Kim, H., Lee, D., Oh, T., Lee, S.W., Choe, Y., Myung, H.: Feature-Based 6-DoF Camera Localization Using Prior Point Cloud and Images. In: Kim, J.-H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications 2. AISC, vol. 274, pp. 3–12. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  11. 11.
    Lee, D., Jung, J., Myung, H.: Pose Graph-Based RGB-D SLAM in Low Dynamic Environments. In: IEEE International Conference on Robotics and Automation, IEEE Press, New York (2014)Google Scholar
  12. 12.
    Lee, D., Myung, H.: Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor. Sensors 14(7), 12467–12496 (2014)CrossRefGoogle Scholar
  13. 13.
    Dellaert, F.: Factor graphs and GTSAM: A hands-on introduction (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Taekjun Oh
    • 1
  • Hyongjin Kim
    • 1
  • Donghwa Lee
    • 1
  • Hyun Chul Roh
    • 2
  • Hyun Myung
    • 1
    • 2
  1. 1.Department of Civil and Environmental EngineeringKAISTDaejeonKorea
  2. 2.Robotics ProgramKAISTDaejeonKorea

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