Motion Trajectory Sequence-Based Map Matching Assisted Indoor Autonomous Mobile Robot Positioning

  • Wenping Yu
  • Jianzhong ZhangEmail author
  • Jingdong Xu
  • Yuwei Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)


Position information is one of basic elements for context awareness of autonomous mobile robots. This paper studies the positioning algorithm of autonomous mobile robots suitable for search and rescue in dark building corridors and underground mine tunnels when an emergency occurs, and proposes a novel map matching aided positioning algorithm based on a Hidden Markov Model. This algorithm does not rely on a camera, and only uses the inertial sensors installed in mobile robot and the indoor map to realize the fusion of dead reckoning and map matching. Firstly, it detects the position-related motion postures during the motion process, and then the motion trajectory is divided into a sub-trajectory sequence. By matching the sub-trajectory sequence with the indoor map, the proposed algorithm achieves tracking and positioning of the mobile robot. In order to verify the effectiveness of the proposed algorithm, this paper adopts four-wheel differentially driven robot to conduct experimental analysis in an actual indoor scenario. The experimental results show that compared with the traditional dead reckoning technology, this algorithm can distinctly reduce the average positioning error of mobile robot, and it is robust to heading angle noises within a certain error range.


Mobile robot Indoor positioning Hidden Markov Model Posture pattern detection 



This work was supported by the National Natural Science Foundation of China (No. 61702288), the Natural Science Foundation of Tianjin in China (No. 16JCQNJC00700) and the Fundamental Research Funds for the Central Universities.


  1. 1.
    Garcia, E., Jimenez, M.A., De Santos, P.G., Armada, M.: The evolution of robotics research. Robot. Autom. Mag. IEEE 14(1), 90–103 (2007)CrossRefGoogle Scholar
  2. 2.
    Wu, J., Li, T.M., Tang, X.Q.: Robust trajectory tracking control of a planar parallel mechanism. J. Tsinghua Univ. 5, 642–646 (2005)Google Scholar
  3. 3.
    Wu, J., Wang, D., Wang, L.: A control strategy of a two degrees-of-freedom heavy duty parallel manipulator. J. Dyn. Syst. Meas. Contr. 137(6), 061007 (2015)CrossRefGoogle Scholar
  4. 4.
    Yang, J., Yang, J., Cai, Z.: An efficient approach to pose tracking based on odometric error modelling for mobile robots. Robotica 33(6), 1231–1249 (2015)CrossRefGoogle Scholar
  5. 5.
    Yuan, X., Wang, D., Yan, Y.: Self-positioning of robot based on dead reckoning and ultrasonic data fusion (in chinese). J. Naval Univ. Eng. 21(5), 67–72 (2009)Google Scholar
  6. 6.
    Yu, N., Wang, S., Xu, C.: RGB-D based autonomous exploration and mapping of a mobile robot in unknown indoor environment. Robot 39(6), 860–871 (2017). (in chinese)Google Scholar
  7. 7.
    Bachrach, A., De Winter, A., He, R., Hemann, G.: Range - robust autonomous navigation in GPS-denied environments. In: IEEE International Conference on Robotics and Automation, pp. 1096–1097. IEEE (2011)Google Scholar
  8. 8.
    Bao, H., Wong, W.C.: An indoor dead-reckoning algorithm with map matching. In: 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1534–1539. IEEE (2013)Google Scholar
  9. 9.
    Tang, H., Chen, W., Wang, J.: Artificial landmark distribution based on multi-ary m-sequence. Robot 36(1), 29–35 (2014). (in chinese)Google Scholar
  10. 10.
    Lu, Y., Song, D.: Visual navigation using heterogeneous landmarks and unsupervised geometric constraints. IEEE Trans. Robotic. 31(3), 736–749 (2015)CrossRefGoogle Scholar
  11. 11.
    Gao, X., Zhang, T.: Unsupervised learning to detect loops using deep neural networks for visual slam system. Auton. Robots 41(1), 1–18 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Kim, J.H., Lee, J.C.: Dead-reckoning scheme for wheeled mobile robots moving on curved surfaces. J. Intell. Robotic Syst. 79(2), 211–220 (2015)CrossRefGoogle Scholar
  13. 13.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robotics 23(1), 34–46 (2007)CrossRefGoogle Scholar
  14. 14.
    Cheng, H., Chen, H., Liu, Y.: Topological indoor localization and navigation for autonomous mobile robot. IEEE Trans. Autom. Sci. Eng. 12(2), 729–738 (2015)CrossRefGoogle Scholar
  15. 15.
    de la Puente, P., Rodríguez-Losada, D.: Feature based graph-slam in structured environments. Auton. Robots 37(3), 243–260 (2014)CrossRefGoogle Scholar
  16. 16.
    Havangi, R., Taghirad, H.D., Nekoui, M.A., Teshnehlab, M.: A square root unscented fastslam with improved proposal distribution and resampling. IEEE Trans. Ind. Electron. 61(5), 2334–2345 (2014)zbMATHCrossRefGoogle Scholar
  17. 17.
    Richter, C., Vega-Brown, W., Roy, N.: Bayesian learning for safe high-speed navigation in unknown environments. In: Bicchi, A., Burgard, W. (eds.) Robotics Research. SPAR, vol. 3, pp. 325–341. Springer, Cham (2018). Scholar
  18. 18.
    Aly, H., Youssef, M.: Semmatch: road semantics-based accurate map matching for challenging positioning data. In: The 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 5. ACM (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wenping Yu
    • 1
  • Jianzhong Zhang
    • 1
    Email author
  • Jingdong Xu
    • 2
  • Yuwei Xu
    • 1
  1. 1.College of Cyberspace SecurityNankai UniversityTianjinChina
  2. 2.College of Computer ScienceNankai UniversityTianjinChina

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