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Biological Cybernetics

, Volume 113, Issue 5–6, pp 515–545 | Cite as

NeuroSLAM: a brain-inspired SLAM system for 3D environments

  • Fangwen Yu
  • Jianga ShangEmail author
  • Youjian Hu
  • Michael Milford
Original Article
  • 534 Downloads

Abstract

Roboticists have long drawn inspiration from nature to develop navigation and simultaneous localization and mapping (SLAM) systems such as RatSLAM. Animals such as birds and bats possess superlative navigation capabilities, robustly navigating over large, three-dimensional environments, leveraging an internal neural representation of space combined with external sensory cues and self-motion cues. This paper presents a novel neuro-inspired 4DoF (degrees of freedom) SLAM system named NeuroSLAM, based upon computational models of 3D grid cells and multilayered head direction cells, integrated with a vision system that provides external visual cues and self-motion cues. NeuroSLAM’s neural network activity drives the creation of a multilayered graphical experience map in a real time, enabling relocalization and loop closure through sequences of familiar local visual cues. A multilayered experience map relaxation algorithm is used to correct cumulative errors in path integration after loop closure. Using both synthetic and real-world datasets comprising complex, multilayered indoor and outdoor environments, we demonstrate NeuroSLAM consistently producing topologically correct three-dimensional maps.

Keywords

Bio-inspired robotics Brain-inspired navigation Simultaneous localization and mapping (SLAM) 3D grid cells Multilayered head direction cells 

Notes

Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2016YFB0502200), the Fundamental Research Founds for National University, China University of Geo-sciences (Wuhan) (No. 1610491T08) and the Hubei Soft Science Research Program (No. QLZX2014010). MM is also partially supported by an ARC Future Fellowship FT140101229. We thank Sourav Garg and Adam Jacobson for their help in improving the comparison experiments. We appreciate the editor and anonymous reviewers for their insightful comments and suggestions on improving the paper.

Supplementary material

422_2019_806_MOESM1_ESM.pdf (4 mb)
Supplementary material 1 (pdf 4130 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Fangwen Yu
    • 1
    • 2
  • Jianga Shang
    • 1
    Email author
  • Youjian Hu
    • 1
  • Michael Milford
    • 2
  1. 1.Faculty of Information EngineeringChina University of Geosciences and National Engineering Research Center for Geographic Information SystemWuhanChina
  2. 2.Science and Engineering FacultyQueensland University of Technology and Australian Centre for Robotic VisionBrisbaneAustralia

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