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MC2SLAM: Real-Time Inertial Lidar Odometry Using Two-Scan Motion Compensation

  • Frank NeuhausEmail author
  • Tilman Koß
  • Robert Kohnen
  • Dietrich Paulus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11269)

Abstract

We propose a real-time, low-drift laser odometry approach that tightly integrates sequentially measured 3D multi-beam LIDAR data with inertial measurements. The laser measurements are motion-compensated using a novel algorithm based on non-rigid registration of two consecutive laser sweeps and a local map. IMU data is being tightly integrated by means of factor-graph optimization on a pose graph. We evaluate our method on a public dataset and also obtain results on our own datasets that contain information not commonly found in existing datasets. At the time of writing, our method was ranked within the top five laser-only algorithms of the KITTI odometry benchmark.

Notes

Acknowledgement

The authors would like to thank three anonymous reviewers for their helpful comments.

Supplementary material

480455_1_En_5_MOESM1_ESM.pdf (3.9 mb)
Supplementary material 1 (pdf 4016 KB)

References

  1. 1.
    Agarwal, S., Mierle, K., et al.: Ceres solver. http://ceres-solver.org
  2. 2.
    Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586–607. International Society for Optics and Photonics (1992)Google Scholar
  3. 3.
    Board, I.: IEEE standard specification format guide and test procedure for single-axis interferometric fiber optic gyros. IEEE Std., pp. 952–1997 (1998)Google Scholar
  4. 4.
    Bosse, M., Zlot, R., Flick, P.: Zebedee: design of a spring-mounted 3-D range sensor with application to mobile mapping. IEEE Trans. Robot. 28(5), 1104–1119 (2012)CrossRefGoogle Scholar
  5. 5.
    Cvišić, I., Petrović, I.: Stereo odometry based on careful feature selection and tracking. In: 2015 European Conference on Mobile Robots, ECMR, pp. 1–6. IEEE (2015)Google Scholar
  6. 6.
    Deschaud, J.: IMLS-SLAM: scan-to-model matching based on 3D data. CoRR abs/1802.08633 (2018). http://arxiv.org/abs/1802.08633
  7. 7.
    Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. In: Proceedings of Robotics: Science and Systems, Rome, Italy, July 2015.  https://doi.org/10.15607/RSS.2015.XI.006
  8. 8.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition, CVPR (2012)Google Scholar
  9. 9.
    Hertzberg, C., Wagner, R., Frese, U., Schröder, L.: Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds. Inf. Fusion 14(1), 57–77 (2013)CrossRefGoogle Scholar
  10. 10.
    Hesch, J.A., Kottas, D.G., Bowman, S.L., Roumeliotis, S.I.: Consistency analysis and improvement of vision-aided inertial navigation. IEEE Trans. Robot. 30(1), 158–176 (2014)CrossRefGoogle Scholar
  11. 11.
    Kaess, M.: Simultaneous localization and mapping with infinite planes. In: ICRA, vol. 1, p. 2 (2015)Google Scholar
  12. 12.
    Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: iSAM2: Incremental smoothing and mapping using the Bayes tree. Int. J. Robot. Res. 31(2), 216–235 (2012)CrossRefGoogle Scholar
  13. 13.
    Kukko, A., Kaartinen, H., Hyyppä, J., Chen, Y.: Multiplatform mobile laser scanning: usability and performance. Sensors 12(9), 11712–11733 (2012)CrossRefGoogle Scholar
  14. 14.
    Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual–inertial odometry using nonlinear optimization. Int. J. Robot. Res. 34(3), 314–334 (2015)CrossRefGoogle Scholar
  15. 15.
    Low, K.L.: Linear least-squares optimization for point-to-plane ICP surface registration, no. 4. Chapel Hill, University of North Carolina (2004)Google Scholar
  16. 16.
    Moosmann, F., Stiller, C.: Velodyne SLAM. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 393–398, Baden-Baden, Germany, June 2011Google Scholar
  17. 17.
    Nüchter, A., Bleier, M., Schauer, J., Janotta, P.: Improving Google’s cartographer 3D mapping by continuous-time SLAM. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 42, 543 (2017)CrossRefGoogle Scholar
  18. 18.
    Nüchter, A., Borrmann, D., Koch, P., Kühn, M., May, S.: A man-portable, IMU-free mobile mapping system. ISPRS Ann. Photogram. Remote Sens. Spat. Inf. Sci. 2, 17–23 (2015). https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W5/17/2015/CrossRefGoogle Scholar
  19. 19.
    Rönnholm, P., Liang, X., Kukko, A., Jaakkola, A., Hyyppä, J.: Quality analysis and correction of mobile backpack laser scanning data. ISPRS Ann. Photogram. Remote Sens. Spat. Inf. Sci. 3, 41 (2016)CrossRefGoogle Scholar
  20. 20.
    Zhang, J., Kaess, M., Singh, S.: Real-time depth enhanced monocular odometry. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014, pp. 4973–4980. IEEE (2014)Google Scholar
  21. 21.
    Zhang, J., Kaess, M., Singh, S.: A real-time method for depth enhanced visual odometry. Auton. Robots 41(1), 31–43 (2017)CrossRefGoogle Scholar
  22. 22.
    Zhang, J., Singh, S.: LOAM: lidar odometry and mapping in real-time. In: Robotics: Science and Systems, vol. 2 (2014)Google Scholar
  23. 23.
    Zhang, J., Singh, S.: Visual-lidar odometry and mapping: low-drift, robust, and fast. In: 2015 IEEE International Conference on Robotics and Automation, ICRA, pp. 2174–2181. IEEE (2015)Google Scholar
  24. 24.
    Zhang, J., Singh, S.: Low-drift and real-time lidar odometry and mapping. Auton. Robots 41(2), 401–416 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Frank Neuhaus
    • 1
    Email author
  • Tilman Koß
    • 1
  • Robert Kohnen
    • 1
  • Dietrich Paulus
    • 1
  1. 1.University of Koblenz-LandauKoblenzGermany

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