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A Parallel RatSlam C++ Library Implementation

Part of the Communications in Computer and Information Science book series (CCIS,volume 1068)

Abstract

RatSlam is a bio-inspired Simultaneous Location and Mapping (SLAM) algorithm used for autonomous mobile robots navigation tasks. This work presents a RatSlam algorithm implementation as a C++ library designed to take advantage of internal RatSlam modules parallelization. The RatSlam algorithm is presented with principal aspects of the library architecture design. Furthermore, its results using a well known RatSlam data set with a standard RatSlam implementation (OpenRatSLAM - Robot Operating System), and a Python implementation. The mapping found with the previous approaches and the proposed on this work were similar. Moreover, the execution times between the OpenRatSLAM and this C++ library was compared, with the proposed implementation having a lower execution time. Thus, the current implementation was validated and has some advantages against previous ones, which can be very relevant for real-time applications.

Keywords

  • RatSlam implementation
  • SLAM algorithm
  • C++ RatSlam library

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  • DOI: 10.1007/978-3-030-36636-0_13
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Notes

  1. 1.

    https://wiki.qut.edu.au/display/cyphy/RatSLAM+MATLAB.

  2. 2.

    https://github.com/mjs513/rsnxt08/tree/wiki.

  3. 3.

    https://github.com/renatopp/ratslam-python.

  4. 4.

    https://github.com/coxlab/ratslam-python.

References

  1. Ball, D., Heath, S., Wiles, J., Wyeth, G., Corke, P., Milford, M.: Openratslam: an open source brain-based slam system. Auton. Robots 34(3), 149–176 (2013). https://doi.org/10.1007/s10514-012-9317-9

    CrossRef  Google Scholar 

  2. Menezes, M.C., de Freitas, E.P., Cheng, S., de Oliveira, A.C.M., de Almeida Ribeiro, P.R.: A neuro-inspired approach to solve a simultaneous location and mapping task using shared information in multiple robots systems. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1753–1758, November 2018. https://doi.org/10.1109/ICARCV.2018.8581270

  3. Milford, M.J., Wyeth, G.F.: Mapping a suburb with a single camera using a biologically inspired slam system. IEEE Trans. Robot. 24(5), 1038–1053 (2008). https://doi.org/10.1109/TRO.2008.2004520

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  5. Milford, M., Wyeth, G., Prasser, D.: Ratslam on the edge: revealing a coherent representation from an overloaded rat brain. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4060–4065. IEEE (2006)

    Google Scholar 

  6. Milford, M.J., Wiles, J., Wyeth, G.F.: Solving navigational uncertainty using grid cells on robots. PLoS Comput. Biol. 6(11), e1000995 (2010)

    MathSciNet  CrossRef  Google Scholar 

  7. Milford, M.J., Wyeth, G.F., Prasser, D.: Ratslam: a hippocampal model for simultaneous localization and mapping. In: 2004 IEEE International Conference on Robotics and Automation, Proceedings, ICRA 2004, vol. 1, pp. 403–408. IEEE (2004)

    Google Scholar 

  8. Ni, J., Wang, C., Fan, X., Yang, S.X.: A bioinspired neural model based extended kalman filter for robot slam. Mathematical Problems in Engineering 2014 (2014)

    Google Scholar 

  9. Saeedi, S., Trentini, M., Seto, M., Li, H.: Multiple-robot simultaneous localization and mapping: a review. J. Field Robot. 33(1), 3–46 (2016)

    CrossRef  Google Scholar 

  10. Silveira, L., et al.: An open-source bio-inspired solution to underwater slam*. IFAC-PapersOnLine 48(2), 212–217 (2015)

    CrossRef  Google Scholar 

  11. Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics In: Intelligent Robotics and Autonomous Agents. The MIT Press (2005)

    Google Scholar 

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Acknowledgment

Our research group acknowledges financial support from FAPEMA (Proc. UNIVERSAL - 01294/16), CAPES/BRAZIL (Finance Code 001) and CNPq/BRAZIL. Sen Cheng was supported by the German Research Foundation (DFG) through SFB 1280, project B14.

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Correspondence to Paulo Rogério de Almeida Ribeiro .

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de Souza Muñoz, M.E. et al. (2019). A Parallel RatSlam C++ Library Implementation. In: Cota, V., Barone, D., Dias, D., Damázio, L. (eds) Computational Neuroscience. LAWCN 2019. Communications in Computer and Information Science, vol 1068. Springer, Cham. https://doi.org/10.1007/978-3-030-36636-0_13

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  • DOI: https://doi.org/10.1007/978-3-030-36636-0_13

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