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Cluster Computing

, Volume 22, Supplement 3, pp 5367–5378 | Cite as

Bionic SLAM based on MEMS pose measurement module and RTAB-Map closed loop detection algorithm

  • MengYuan ChenEmail author
Article
  • 183 Downloads

Abstract

In the complex indoor scene, the RatSLAM, a rodent model navigation algorithm, will suffer a performance reduction due to light changes or other factors. Based on this the RTAB-Map closed loop detection strategy is introduced into the RatSLAM system, which can, through closed loop detection, eliminate the accumulative errors cause by the experience map of pose cells and local view cells, and thus to improve the instability of performance due to light changes or other factors. However complex scene, such as moving obstructions, will lead to mistakes in the visual odometer’s identification of speeds and thus cause conspicuous skewing of the navigation trail, which sometimes cannot be corrected through scene reorientation. This paper proposes a RatSLAM model with pose measurement module and RTAB-Map closed loop detection algorithm. The improvements are as follows. First, by fusing RTAB-Map closed loop detection, the phenomenon of odometer drifting under RatSLAM bionic algorithm due to error accumulation of friction or other factors like light changes can be improved; second, RTAB-Map algorithm itself improves the system real-time performance by using four kinds of memorizers; last but not least, the fusion of pose measuring module can prevent emergent obstruction from disturbing the visual odometer to obtain speed information, and it is more accurate when combined with sensor technology.

Keywords

Mobile robot Simultaneous localization and mapping Rodent model Composite navigation model Key frame matching closed loop detection 

Notes

Acknowledgements

This work was supported by the Key Project of Natural Science by Education Department of Anhui Province (No. KJ2016A794).

References

  1. 1.
    Welch, G., Bishop, G.: An Introduction to the Kalman filter, vol. 8, pp. 127–132. University of North Carolina, Chapel Hill (1995)Google Scholar
  2. 2.
    Bian, M., Wang, J., Liu, W.: Robust and reliable estimation via recursive nonlinear dynamic data reconciliation based on cubature Kalman filter. Clust. Comput. 6, 1–11 (2017)Google Scholar
  3. 3.
    Smith, R.C., Cheeseman, P.: On the representation and estimation of spatial uncertainly. Int. J. Robot. Res. 5(4), 56–68 (1987)CrossRefGoogle Scholar
  4. 4.
    Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. Mach. Intell. Pattern Recognit. 1(5), 435–461 (1986)zbMATHGoogle Scholar
  5. 5.
    Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004)CrossRefGoogle Scholar
  6. 6.
    Julier, S., Uhlmann, J., Durrant-Whyte, H.F.: A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. Autom. Control 45(3), 477–482 (2000)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Arasaratnam, I., Haykin, S.: Cubature Kalman filters. IEEE Trans. Autom. Control 54(6), 1254–1269 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Thrun, S., Fox, D., Burgard, W.: Robust Monte Carlo localization for mobile robots. Artif. Intell. 128(1), 99–141 (2001)CrossRefGoogle Scholar
  9. 9.
    Montemerlo, M., Thrun, S., Whittaker, W.: Conditional particle filters for simultaneous mobile robot localization and people-tracking. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 695–701 (2002)Google Scholar
  10. 10.
    Wang, H., Li, J., Hou, Z.: Research on parallelized real-time map matching algorithm for massive GPS data. Clust. Comput. 2, 1–12 (2017)Google Scholar
  11. 11.
    Abid, M., Ishtiaq, M., Khan, F.A.: Computationally efficient generic adaptive filter (CEGAF)[J]. Clust. Comput. 3, 1–11 (2017)Google Scholar
  12. 12.
    Llorca, D.F., Quintero, R., Parra, I.: Recognizing individuals in groups in outdoor environments combining stereo vision. RFID and BLE. Clust. Comput. 20(1), 769–779 (2017)CrossRefGoogle Scholar
  13. 13.
    Jia, Z., Chen, Z., Wang, D.: Time series analysis of carrier phase differences for dual-frequency GPS high-accuracy positioning. Clust. Comput. 19(3), 1461–1474 (2016)CrossRefGoogle Scholar
  14. 14.
    Milford, M.J., Prasser, D.P., Wyeth, G.F.: Effect of representation size and visual ambiguity on RatSLAM system performance. In: Australasian Conference on Robotics and Automation. Australian Robotics and Automation Society (ARAA), pp. 1–8 (2006)Google Scholar
  15. 15.
    Milford, M., Schulz, R., Prasser, D.: Learning spatial concepts from RatSLAM representations. Robot. Auton. Syst. 55(5), 403–410 (2007)CrossRefGoogle Scholar
  16. 16.
    Milford, M., Wyeth, G., Prasser, D.: RatSLAM on the edge: revealing a coherent representation from an overloaded rat brain. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp. 4060–4065 (2007)Google Scholar
  17. 17.
    Milford, M., Wyeth, G.: Persistent navigation and mapping using a biologically inspired SLAM system. Int. J. Robot. Res. 29(9), 1131–1153 (2010)CrossRefGoogle Scholar
  18. 18.
    Milford, M.J., Schill, F., Corke, P, et al.: Aerial SLAM with a single camera using visual expectation. In: IEEE International Conference on Robotics & Automation, IEEE, pp. 2506–2512 (2011)Google Scholar
  19. 19.
    Zhang, X., Hu, X., Zhang, L.: An improved bionic navigation algorithm based on RatSLAM. Navig. Control 14(5), 73–80 (2015)Google Scholar
  20. 20.
    Glover, A.J., Maddern, W.P., Milford, M.J., et al. FAB-MAP + RatSLAM: appearance-based SLAM for multiple times of day. In: IEEE International Conference on Robotics and Automation, IEEE, pp. 3507–3512 (2010)Google Scholar
  21. 21.
    Berkvens, R., Vercauteren, C., Peremans, H.: Feasibility of geomagnetic localization and geomagnetic RatSLAM. Int. J. Adv. Syst. Meas. 7(1), 44–56 (2014)Google Scholar
  22. 22.
    Berkvens, R., Jacobson, A., Milford, M., et al.: Biologically inspired SLAM using Wi-Fi. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp. 1804–1811 (2014)Google Scholar
  23. 23.
    Berkvens, R., Weyn, M., Peremans, H.: Asynchronous, electromagnetic sensor fusion in RatSLAM. In: IEEE Sensors, pp. 1–4 (2015)Google Scholar
  24. 24.
    Labbe, M., Michaud, F.: Memory management for real-time appearance-based loop closure detection. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp. 1271–1276 (2011)Google Scholar
  25. 25.
    Labbe, M., Michaud, F.: Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Transactions on Robotics 29(3), 734–745 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Key Lab of Electric Drive and Control of Anhui ProvinceAnhui Polytechnic UniversityWuhuChina
  2. 2.Department of Precision Machinery and Precision InstrumentationUniversity of Science and Technology of ChinaHefeiChina

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