AN IMPROVED VISION-BASED SLAM APPROACH INSPIRED FROM ANIMAL SPATIAL COGNITION

Jianjun Ni, Yan Chen, Kang Wang, and Simon X. Yang

References

  1. [1] Z. Huang, J. Zhu, L. Yang, B. Xue, J. Wu, and Z. Zhao,Accurate 3-D position and orientation method for indoormobile robot navigation based on photoelectric scanning, IEEETransactions on Instrumentation and Measurement, 64(9),2015, 2518–2529.
  2. [2] Y. Zhuang, K. Wang, W. Wang, and H. Hu, A hybrid sensingapproach to mobile robot localization in complex indoor envi-ronments, International Journal of Robotics and Automation,27(2), 2012, 198–205.
  3. [3] B. Gozick, K.P. Subbu, R. Dantu, and T. Maeshiro, Magneticmaps for indoor navigation, IEEE Transactions on Instrumen-tation and Measurement, 60(12), 2011, 3883–3891.
  4. [4] H. Abdelnasser, R. Mohamed, A. Elgohary, M.F. Alzantot,H. Wang, S. Sen, R.R. Choudhury, and M. Youssef,SemanticSLAM: Using environment landmarks for unsu-pervised indoor localization, IEEE Transactions on MobileComputing, 15(7), 2016, 1770–1782.
  5. [5] Z. Liang and Y. Chen, Closed-loop detection algorithm usingvisualwords, International Journal of Robotics and Automa-tion, 29(2), 2014, 155–161.
  6. [6] Y. Sun, M. Liu, and M.Q.-H. Meng, Improving RGB-DSLAM in dynamic environments: A motion removal approach,Robotics and Autonomous Systems, 89, 2017, 110–122.
  7. [7] C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza,J. Neira, I. Reid, and J.J. Leonard, Past, present, and futureof simultaneous localization and mapping: Toward the robust-perception age, IEEE Transactions on Robotics, 32(6), 2016,1309–1332.
  8. [8] M. Balcilar, S. Yavuz, M.F. Amasyali, E. Uslu, and F. Cakmak,R-SLAM: Resilient localization and mapping in challengingenvironments, Robotics and Autonomous Systems, 87, 2017,66–80.
  9. [9] J. Cheng, J. Kim, Z. Jiang, and W. Che, Dual quaternion-based graphical SLAM, Robotics and Autonomous Systems,77, 2016, 15–24.
  10. [10] S. Chen and C. Chen, Probabilistic fuzzy system for uncertainlocalization and map building of mobile robots, IEEE Trans-actions on Instrumentation and Measurement, 61(6), 2012,1546–1560.
  11. [11] P. De La Puente and D. Rodriguez-Losada, Feature basedgraph-SLAM in structured environments, Autonomous Robots,37(3), 2014, 243–260.
  12. [12] J. Ni, L. Wu, X. Fan, and S.X. Yang, Bioinspired intelli-gent algorithm and its applications for mobile robot control:A survey, Computational Intelligence and Neuroscience, 2016,2016. DOI: 10.1155/2016/3810903.
  13. [13] M. Milford and G. Wyeth, Persistent navigation and map-ping using a biologically inspired SLAM system, InternationalJournal of Robotics Research, 29(9), 2010, 1131–1153.
  14. [14] J. Ni, C. Wang, X. Fan, and S.X. Yang, A bioinspired neu-ral model based extended Kalman filter for robot SLAM,501Mathematical Problems in Engineering, 2014, 2014. DOI:10.1155/2014/905826.
  15. [15] Y. Li, S. Li, and Y. Ge, A biologically inspired solution tosimultaneous localization and consistent mapping in dynamicenvironments, Neurocomputing, 104, 2013, 170–179.
  16. [16] D.S. Pata, A. Escuredo, S. Lallee, and P.F.M.J. Verschure,Hippocampal based model reveals the distinct roles of dentategyrus and CA3 during robotic spatial navigation, Lecture Notesin Computer Science, 8608, 2014, 273–283.
  17. [17] T. Oess, J.L. Krichmar, and F. Rohrbein, A computationalmodel for spatial navigation based on reference frames in thehippocampus, retrosplenial cortex, and posterior parietal cor-tex, Frontiers in Neurorobotics, 11, 2017. DOI: 10.3389/fnbot.2017.00004.
  18. [18] F. Sargolini, M. Fyhn, T. Hafting, B.L. McNaughton, M.P.Witter, M.-B. Moser, and E.I. Moser, Conjunctive represen-tation of position, direction, and velocity in entorhinal cortex,Science, 312(5774), 2006, 758–762.
  19. [19] A. Stepanyuk, Self-organization of grid fields under supervisionof place cells in a neuron model with associative plasticity,Biologically Inspired Cognitive Architectures, 13, 2015, 48–62.
  20. [20] M. Sonka, V. Hlavac, and R. Boyle, Image processing, analysisand machine vision (Boston, MA: Springer, 1993).
  21. [21] M.J. Milford and G.F. Wyeth, Mapping a suburb with a singlecamera using a biologically inspired SLAM system, IEEETransactions on Robotics, 24(5), 2008, 1038–1053.
  22. [22] U.M. Erdem, M.J. Milford, and M.E. Hasselmo, A hierarchicalmodel of goal directed navigation selects trajectories in a visualenvironment, Neurobiology of Learning & Memory, 117, 2015,109–121.
  23. [23] S.M. Stringer, T.P. Trappenberg, E.T. Rolls, and I.E.de Araujo, Self-organizing continuous attractor networks andpath integration: one-dimensional models of head directioncells, Network: Computation in Neural Systems, 13(4), 2002,217–242.
  24. [24] B. Postle, Working memory as an emergent property of themind and brain, Neuroscience, 139(1), 2006, 23–38.
  25. [25] S. Lewandowsky, K. Oberauer, and G.D. Brown, No tempo-ral decay in verbal short-term memory, Trends in CognitiveSciences, 13(3), 2009, 120–126.
  26. [26] M. Milford and G. Wyeth, Hippocampal models for simulta-neous localisation and mapping on an autonomous robot, inAustralasian Conference on Robotics and Automation 2003,Brisbane, Queensland, December 1–3, 2003, pp. 1–10.
  27. [27] M. Milford, R. Schulz, D. Prasser, G. Wyeth, and J. Wiles,Learning spatial concepts from RatSLAM representations,Robotics and Autonomous Systems, 55(5), 2007, 403–410.
  28. [28] D. Ball, S. Heath, J. Wiles, G. Wyeth, P. Corke, and M. Milford,OpenRatSLAM: An open source brain-based SLAM system,Autonomous Robots, 34(3), 2013, 149–176.
  29. [29] J. Ni, L. Yang, Z. Mo, X. Fan, and C. Luo, An improved spinalneural system based method for mobile robot navigation, in 3rdInternational Conference on Fuzzy Systems and Data Mining,Hualien, Taiwan, November 24–27, 2017, pp. 337–342.

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