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

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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.

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References

  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. 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. Smith, R.C., Cheeseman, P.: On the representation and estimation of spatial uncertainly. Int. J. Robot. Res. 5(4), 56–68 (1987)

    Article  Google Scholar 

  4. Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. Mach. Intell. Pattern Recognit. 1(5), 435–461 (1986)

    MATH  Google Scholar 

  5. Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  7. Arasaratnam, I., Haykin, S.: Cubature Kalman filters. IEEE Trans. Autom. Control 54(6), 1254–1269 (2009)

    Article  MathSciNet  Google Scholar 

  8. Thrun, S., Fox, D., Burgard, W.: Robust Monte Carlo localization for mobile robots. Artif. Intell. 128(1), 99–141 (2001)

    Article  Google Scholar 

  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)

  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. Abid, M., Ishtiaq, M., Khan, F.A.: Computationally efficient generic adaptive filter (CEGAF)[J]. Clust. Comput. 3, 1–11 (2017)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  15. Milford, M., Schulz, R., Prasser, D.: Learning spatial concepts from RatSLAM representations. Robot. Auton. Syst. 55(5), 403–410 (2007)

    Article  Google Scholar 

  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)

  17. Milford, M., Wyeth, G.: Persistent navigation and mapping using a biologically inspired SLAM system. Int. J. Robot. Res. 29(9), 1131–1153 (2010)

    Article  Google Scholar 

  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)

  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. 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)

  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. 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)

  23. Berkvens, R., Weyn, M., Peremans, H.: Asynchronous, electromagnetic sensor fusion in RatSLAM. In: IEEE Sensors, pp. 1–4 (2015)

  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)

  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)

    Article  Google Scholar 

Download references

Acknowledgements

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

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Authors and Affiliations

  1. Key Lab of Electric Drive and Control of Anhui Province, Anhui Polytechnic University, Wuhu, 241000, China

    MengYuan Chen

  2. Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China

    MengYuan Chen

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  1. MengYuan Chen
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Correspondence to MengYuan Chen.

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Chen, M. Bionic SLAM based on MEMS pose measurement module and RTAB-Map closed loop detection algorithm. Cluster Comput 22 (Suppl 3), 5367–5378 (2019). https://doi.org/10.1007/s10586-017-1246-0

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  • DOI: https://doi.org/10.1007/s10586-017-1246-0

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