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


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.


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



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)


  1. Arleo A, Gerstner W (2000) Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity. Biol Cybern 83(3):287–299. CrossRefPubMedGoogle Scholar
  2. Ball D, Heath S, Wiles J, Wyeth G, Corke P, Milford M (2013) Openratslam: an open source brain-based slam system. Auton Robots 34(3):149–176. CrossRefGoogle Scholar
  3. Banino A, Barry C, Uria B, Blundell C, Lillicrap TP, Mirowski P, Pritzel A, Chadwick MJ, Degris T, Modayil J, Wayne G, Soyer H, Viola F, Zhang B, Goroshin R, Rabinowitz NC, Pascanu R, Beattie C, Petersen S, Sadik A, Gaffney S, King H, Kavukcuoglu K, Hassabis D, Hadsell R, Kumaran D (2018) Vector-based navigation using grid-like representations in artificial agents. Nature 557(7705):429–433. CrossRefPubMedGoogle Scholar
  4. Barrera A, Weitzenfeld A (2008) Biologically-inspired robot spatial cognition based on rat neurophysiological studies. Auton Robots 25(1–2):147–169. CrossRefGoogle Scholar
  5. Behley J, Stachniss C (2018) Efficient surfel-based SLAM using 3D laser range data in urban environments. In: Robotics: science and systems.
  6. Bellingham J, Dupont PE, Fischer P, Floridi L, Full R, Jacobstein N, Kumar V, McNutt M, Merrifield RD, Nelson BJ, Scassellati B, Taddeo M, Taylor R, Veloso MM, Wang ZL, Wood RJ (2018) The grand challenges of science robotics. Sci Robot 3(14):eaar7650. CrossRefGoogle Scholar
  7. Bjerknes TL, Dagslott NC, Moser EI, Moser MB (2018) Path integration in place cells of developing rats. Proc Natl Acad Sci 115(7):E1637–E1646. CrossRefPubMedGoogle Scholar
  8. Burak Y, Fiete IR (2009) Accurate path integration in continuous attractor network models of grid cells. PLoS Comput Biol 5(2):e1000291. CrossRefPubMedPubMedCentralGoogle Scholar
  9. Cadena C, Carlone L, Carrillo H, Latif Y, Scaramuzza D, Neira J, Reid I, Leonard JJ (2016) Past, present, and future of simultaneous localization and mapping: roward the robust-perception age. IEEE Trans Robot 32(6):1309–1332. CrossRefGoogle Scholar
  10. Campbell MG, Ocko SA, Mallory CS, Low IIC, Ganguli S, Giocomo LM (2018) Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation. Nat Neurosci 21(8):1096–1106. CrossRefPubMedPubMedCentralGoogle Scholar
  11. Casali G, Bush D, Jeffery K (2019) Altered neural odometry in the vertical dimension. In: Proceedings of the national academy of sciences, p 201811867. CrossRefGoogle Scholar
  12. Cope AJ, Sabo C, Vasilaki E, Barron AB, Marshall JAR (2017) A computational model of the integration of landmarks and motion in the insect central complex. PLOS ONE 12(2):e0172325. CrossRefPubMedPubMedCentralGoogle Scholar
  13. Cummins MJ, Newman P (2008) FAB-MAP: probabilistic localization and mapping in the space of appearance. Int J Robot Res 27(6):647–665. CrossRefGoogle Scholar
  14. Davison AJ, Reid ID, Molton ND, Stasse O (2007) MonoSLAM: real-time single camera SLAM. IEEE Trans Pattern Anal Mach Intell 29(6):1052–1067. CrossRefPubMedGoogle Scholar
  15. Dissanayake MG, Newman P, Clark S, Durrant-Whyte HF, Csorba M (2001) A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans Robot Autom 17(3):229–241. CrossRefGoogle Scholar
  16. Droeschel D, Schwarz M, Behnke S (2017) Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner. Robot Auton Syst 88:104–115. CrossRefGoogle Scholar
  17. Dupeyroux J, Serres JR, Viollet S (2019) AntBot: a six-legged walking robot able to home like desert ants in outdoor environments. Sci Robot 4(27):eaau0307. CrossRefGoogle Scholar
  18. Endres F, Hess J, Sturm J, Cremers D, Burgard W (2014) 3-D mapping with an RGB-D camera. IEEE Trans Robot 30(1):177–187. CrossRefGoogle Scholar
  19. Engel J, Schöps T, Cremers D (2014) LSD-SLAM: large-scale direct monocular SLAM. In: European Conference on computer vision. Springer, Berlin, pp 834–849.
  20. Engel J, Koltun V, Cremers D (2018) Direct sparse odometry. IEEE Trans Pattern Anal Mach Intell 40(3):611–625. CrossRefPubMedGoogle Scholar
  21. Evans T, Bicanski A, Bush D, Burgess N (2016) How environment and self-motion combine in neural representations of space. J Physiol 594(22):6535–6546. CrossRefPubMedPubMedCentralGoogle Scholar
  22. Evers C, Naylor PA (2018) Acoustic SLAM. IEEE/ACM Trans Audio Speech Lang Process 26(9):1484–1498. CrossRefGoogle Scholar
  23. Faessler M, Fontana F, Forster C, Mueggler E, Pizzoli M, Scaramuzza D (2016) Autonomous, vision-based flight and live dense 3D mapping with a quadrotor micro aerial vehicle. J Field Robot 33:431–450. CrossRefGoogle Scholar
  24. Finkelstein A, Derdikman D, Rubin A, Foerster JN, Las L, Ulanovsky N (2015) Three-dimensional head-direction coding in the bat brain. Nature 517(4):159–164. CrossRefPubMedGoogle Scholar
  25. Finkelstein A, Las L, Ulanovsky N (2016) 3-D maps and compasses in the brain. Annu Rev Neurosci 39(1):171–96. CrossRefPubMedGoogle Scholar
  26. Finkelstein A, Ulanovsky N, Tsodyks M, Aljadeff J (2018) Optimal dynamic coding by mixed-dimensionality neurons in the head-direction system of bats. Nat Commun 9(1):350. CrossRefGoogle Scholar
  27. Forster C, Pizzoli M, Scaramuzza D (2014) SVO: fast semi-direct monocular visual odometry. In: 2014 IEEE international conference on robotics and automation (ICRA), pp 15–22.
  28. Forster C, Zhang Z, Gassner M, Werlberger M, Scaramuzza D (2017) SVO: semidirect visual odometry for monocular and multicamera systems. IEEE Trans Robot 33(2):249–265. CrossRefGoogle Scholar
  29. Gallego G, Lund JEA, Mueggler E, Rebecq H, Delbrück T, Scaramuzza D (2018) Event-based, 6-DOF camera tracking from photometric depth maps. IEEE Trans Pattern Anal Mach Intell 40(10):2402–2412. CrossRefPubMedGoogle Scholar
  30. Gao X, Wang R, Demmel N, Cremers D (2018) LDSO: direct sparse odometry with loop closure. In: 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 2198–2204Google Scholar
  31. Gaussier P, Banquet JP, Cuperlier N, Quoy M, Aubin L, Jacob PY, Sargolini F, Save E, Krichmar JL, Poucet B (2019) Merging information in the entorhinal cortex: What can we learn from robotics experiments and modeling? J Exp Biol 222(Suppl 1):jeb186932. CrossRefPubMedGoogle Scholar
  32. Geiger A, Ziegler J, Stiller C (2011) Stereoscan: dense 3D reconstruction in real-time. In: 2011 IEEE intelligent vehicles symposium (IV), pp 963–968.
  33. Gianelli S, Harland B, Fellous JM (2018) A new rat-compatible robotic framework for spatial navigation behavioral experiments. J Neurosci Methods 294:40–50. CrossRefPubMedGoogle Scholar
  34. Giovannangeli C, Gaussier P (2008) Autonomous vision-based navigation: goal-oriented action planning by transient states prediction, cognitive map building, and sensory-motor learning. In: 2008 IEEE/RSJ International conference on intelligent robots and systems, pp 676–683.
  35. Hafting T, Fyhn M, Molden S, Moser MB, Moser EI (2005) Microstructure of a spatial map in the entorhinal cortex. Nature 436(7052):801–806. CrossRefPubMedGoogle Scholar
  36. Hayman RMA, Casali G, Wilson JJ, Jeffery KJ (2015) Grid cells on steeply sloping terrain: evidence for planar rather than volumetric encoding. Front Psychol 6:925. CrossRefPubMedPubMedCentralGoogle Scholar
  37. Henry P, Krainin M, Herbst E, Ren X, Fox D (2012) RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments. Int J Robot Res 31(5):647–663. CrossRefGoogle Scholar
  38. Horiuchi TK, Moss CF (2015) Grid cells in 3-D: reconciling data and models. Hippocampus 25(12):1489–1500. CrossRefPubMedGoogle Scholar
  39. Jauffret A, Cuperlier N, Gaussier P (2015) From grid cells and visual place cells to multimodal place cell: a new robotic architecture. Front Neurorobot 9:1. CrossRefPubMedPubMedCentralGoogle Scholar
  40. Jeffery KJ, Jovalekic A, Verriotis M, Hayman R (2013) Navigating in a three-dimensional world. Behav Brain Sci 36(05):523–543. CrossRefPubMedGoogle Scholar
  41. Jeffery KJ, Wilson JJ, Casali G, Hayman RM (2015) Neural encoding of large-scale three-dimensional space-properties and constraints. Front Psychol 6:927. CrossRefPubMedPubMedCentralGoogle Scholar
  42. Jeffery KJ, Page HJI, Stringer SM (2016) Optimal cue combination and landmark-stability learning in the head direction system. J Physiol 594(22):6527–6534. CrossRefPubMedPubMedCentralGoogle Scholar
  43. Karrer M, Schmuck P, Chli M (2018) CVI-SLAM—collaborative visual-inertial SLAM. IEEE Robot Autom Lett 3(4):2762–2769. CrossRefGoogle Scholar
  44. Kim M, Maguire EA (2018a) Encoding of 3D head direction information in the human brain. Hippocampus 29:619–629. CrossRefPubMedPubMedCentralGoogle Scholar
  45. Kim M, Maguire EA (2018b) Hippocampus, retrosplenial and parahippocampal cortices encode multicompartment 3D space in a hierarchical manner. Cereb Cortex 28(5):1898–1909. CrossRefPubMedPubMedCentralGoogle Scholar
  46. Kim M, Maguire EA (2019) Can we study 3D grid codes non-invasively in the human brain? Methodological considerations and fMRI findings. NeuroImage 186:667–678. CrossRefPubMedPubMedCentralGoogle Scholar
  47. Kim M, Jeffery KJ, Maguire EA (2017) Multivoxel pattern analysis reveals 3D place information in the human hippocampus. J Neurosci 37(16):4270–4279. CrossRefPubMedPubMedCentralGoogle Scholar
  48. Klein G, Murray DW (2007) Parallel tracking and mapping for small AR workspaces. In: 2007 6th IEEE and ACM international symposium on mixed and augmented reality, pp 225–234.
  49. Konolige K, Agrawal M (2008) FrameSLAM: from bundle adjustment to real-time visual mapping. IEEE Trans Robot 24(5):1066–1077. CrossRefGoogle Scholar
  50. Kreiser R, Cartiglia M, Martel JN, Conradt J, Sandamirskaya Y (2018a) A neuromorphic approach to path integration: a head-direction spiking neural network with vision-driven reset. In: 2018 IEEE international symposium on circuits and systems (ISCAS), pp 1–5.
  51. Kreiser R, Renner A, Sandamirskaya Y, Pienroj P (2018b) Pose estimation and map formation with spiking neural networks: towards neuromorphic SLAM. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 2159–2166.
  52. Krombach N, Droeschel D, Houben S, Behnke S (2018) Feature-based visual odometry prior for real-time semi-dense stereo SLAM. Robot Auton Syst 109:38–58. CrossRefGoogle Scholar
  53. Kropff E, Carmichael JE, Moser MB, Moser EI (2015) Speed cells in the medial entorhinal cortex. Nature 523(7561):419–424. CrossRefPubMedGoogle Scholar
  54. Laurens J, Angelaki DE (2018) The brain compass: a perspective on how self-motion updates the head direction cell attractor. Neuron 97(2):275–289. CrossRefPubMedPubMedCentralGoogle Scholar
  55. Laurens J, Kim B, Dickman JD, Angelaki DE (2016) Gravity orientation tuning in macaque anterior thalamus. Nat Neurosci 19(12):1566–1568. CrossRefPubMedPubMedCentralGoogle Scholar
  56. Lever C, Burton S, Jeewajee A, O’Keefe J, Burgess N (2009) Boundary vector cells in the subiculum of the hippocampal formation. J Neurosci 29(31):9771–9777. CrossRefPubMedPubMedCentralGoogle Scholar
  57. Llofriu M, Tejera G, Contreras M, Pelc T, Fellous J, Weitzenfeld A (2015) Goal-oriented robot navigation learning using a multi-scale space representation. Neural Netw 72:62–74. CrossRefPubMedGoogle Scholar
  58. Lowry SM, Sünderhauf N, Newman P, Leonard JJ, Cox DD, Corke PI, Milford M (2016) Visual place recognition: a survey. IEEE Trans Robot 32(1):1–19. CrossRefGoogle Scholar
  59. Lynen S, Bosse M, Siegwart R (2016) Keyframe-based visual–inertial odometry using nonlinear optimization. Int J Robot Res 124(1):49–64. CrossRefGoogle Scholar
  60. Maddern WP, Milford M, Wyeth G (2012) CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory. Int J Robot Res 31(4):429–451. CrossRefGoogle Scholar
  61. Matsuki H, von Stumberg L, Usenko VC, Stuckler J, Cremers D (2018) Omnidirectional DSO: direct sparse odometry with fisheye cameras. IEEE Robot Autom Lett 3(4):3693–3700. CrossRefGoogle Scholar
  62. McNaughton BL, Battaglia FP, Jensen O, Moser EI, Moser MB (2006) Path integration and the neural basis of the ’cognitive map’. Nat Rev Neurosci 7(8):663–678. CrossRefPubMedGoogle Scholar
  63. Meyer JA, Guillot A, Girard B, Khamassi M, Pirim P, Berthoz A (2005) The Psikharpax project: towards building an artificial rat. Robot Auton Syst 50(4):211–223. CrossRefGoogle Scholar
  64. Milford M (2013) Vision-based place recognition: How low can you go? Int J Robot Res 32(7):766–789. CrossRefGoogle Scholar
  65. Milford M, Schulz R (2014) Principles of goal-directed spatial robot navigation in biomimetic models. Philos Trans R Soc B Biol Sci 369(1655):20130484. CrossRefGoogle Scholar
  66. Milford M, Wyeth G (2008) Mapping a suburb with a single camera using a biologically inspired SLAM system. IEEE Trans Robot 24(5):1038–1053. CrossRefGoogle Scholar
  67. Milford M, Wyeth G (2010) Persistent navigation and mapping using a biologically inspired SLAM system. Int J Robot Res 29(9):1131–1153. CrossRefGoogle Scholar
  68. Milford M, Wyeth G (2012) SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights. In: 2012 IEEE international conference on robotics and automation, pp 1643–1649.
  69. Milford MJ, Wyeth GF, Prasser D (2004) RatSLAM: a hippocampal model for simultaneous localization and mapping. In: 2004 IEEE international conference on robotics and automation (ICRA). IEEE, vol 1, pp 403–408.
  70. Milford M, McKinnon D, Warren M, Wyeth G, Upcroft B (2011a) Feature-based visual odometry and featureless place recognition for SLAM in 2.5 d environments. In: In Drummond, Tom (eds.) ACRA 2011 Proceedings, Australian robotics & automation association, robotics: science and systems foundation, pp 1–8.
  71. Milford M, Schill F, Corke PI, Mahony RE, Wyeth G (2011b) Aerial SLAM with a single camera using visual expectation. In: 2011 IEEE international conference on robotics and automation, pp 2506–2512.
  72. Montemerlo M, Thrun S, Koller D, Wegbreit B, et al (2002) FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: Proceedings of the national conference on artificial intelligence (AAAI)Google Scholar
  73. Moser EI, Moser MB, McNaughton BL (2017) Spatial representation in the hippocampal formation: a history. Nat Neurosci 20(11):1448–1464. CrossRefPubMedGoogle Scholar
  74. Mulas M, Waniek N, Conradt J (2016) Hebbian plasticity realigns grid cell activity with external sensory cues in continuous attractor models. Front Comput Neurosci 10:13. CrossRefPubMedPubMedCentralGoogle Scholar
  75. Mur-Artal R, Tardós JD (2017) Orb-slam2: an open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans Robot 33(5):1255–1262. CrossRefGoogle Scholar
  76. Mur-Artal R, Montiel JMM, Tardos JD (2015) ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans Robot 31(5):1147–1163. CrossRefGoogle Scholar
  77. Naseer T, Burgard W, Stachniss C (2018) Robust visual localization across seasons. IEEE Trans Robot 34(2):289–302. CrossRefGoogle Scholar
  78. Newcombe RA, Lovegrove S, Davison AJ (2011) DTAM: dense tracking and mapping in real-time. In: 2011 International conference on computer vision, pp 2320–2327.
  79. O’Keefe J, Dostrovsky J (1971) The hippocampus as a spatial map: preliminary evidence from unit activity in the freely-moving rat. Brain Res 34(1):171–175. CrossRefPubMedGoogle Scholar
  80. Page HJI, Wilson JJ, Jeffery KJ (2018) A dual-axis rotation rule for updating the head direction cell reference frame during movement in three dimensions. J Neurophysiol 119(1):192–208. CrossRefPubMedGoogle Scholar
  81. Paul R, Newman P (2010) FAB-MAP 3D: topological mapping with spatial and visual appearance. In: 2010 IEEE international conference on robotics and automation, pp 2649–2656.
  82. Qin T, Li P, Shen S (2018) Vins-mono: a robust and versatile monocular visual–inertial state estimator. IEEE Trans Robot 34(4):1004–1020. CrossRefGoogle Scholar
  83. Rebecq H, Horstschaefer T, Gallego G, Scaramuzza D (2017) EVO: a geometric approach to event-based 6-DOF parallel tracking and mapping in real time. IEEE Robot Autom Lett 2(2):593–600. CrossRefGoogle Scholar
  84. Sabo CM, Cope A, Gurney K, Vasilaki E, Marshall J (2016) Bio-inspired visual navigation for a quadcopter using optic flow. In: AIAA Infotech @ Aerospace, American Institute of Aeronautics and Astronautics.
  85. Sabo C, Yavuz E, Cope A, Gumey K, Vasilaki E, Nowotny T, Marshall JAR (2017) An inexpensive flying robot design for embodied robotics research. In: 2017 International joint conference on neural networks (IJCNN), IEEE. IEEE, pp 4171–4178.
  86. Samsonovich A, McNaughton BL (1997) Path integration and cognitive mapping in a continuous attractor neural network model. J Neurosci 17(15):5900–5920. CrossRefPubMedPubMedCentralGoogle Scholar
  87. Saputra MRU, Markham A, Trigoni N (2018) Visual SLAM and structure from motion in dynamic environments: a survey. ACM Comput Surv 51(2):1–36. CrossRefGoogle Scholar
  88. Schneider T, Dymczyk M, Fehr M, Egger K, Lynen S, Gilitschenski I, Siegwart R (2018) maplab: an open framework for research in visual–inertial mapping and localization. IEEE Robot Autom Lett 3(3):1418–1425CrossRefGoogle Scholar
  89. Shinder ME, Taube JS (2019) Three-dimensional tuning of head direction cells in rats. J Neurophysiol 121(1):4–37. CrossRefPubMedGoogle Scholar
  90. Shipston-Sharman O, Solanka L, Nolan MF (2016) Continuous attractor network models of grid cell firing based on excitatory–inhibitory interactions. J Physiol 594(22):6547–6557. CrossRefPubMedPubMedCentralGoogle Scholar
  91. Silveira L, Guth F, Drews P, Botelho S (2013) 3D robotic mapping: a biologic approach. In: 2013 16th international conference on advanced robotics (ICAR), IEEE. IEEE, pp 1–6.
  92. Silveira L, Guth F, Drews-Jr P, Ballester P, Machado M, Codevilla F, Duarte-Filho N, Botelho S (2015) An open-source bio-inspired solution to underwater SLAM. IFAC-PapersOnLine 48(2):212–217. CrossRefGoogle Scholar
  93. Solstad T, Boccara CN, Kropff E, Moser MB, Moser EI (2008) Representation of geometric borders in the entorhinal cortex. Science 322(5909):1865–1868. CrossRefPubMedGoogle Scholar
  94. Soman K, Chakravarthy S, Yartsev MM (2018) A hierarchical anti-Hebbian network model for the formation of spatial cells in three-dimensional space. Nat Commun 9(1):4046. CrossRefPubMedPubMedCentralGoogle Scholar
  95. Stackman RW, Tullman ML, Taube JS (2000) Maintenance of rat head direction cell firing during locomotion in the vertical plane. J Neurophysiol 83(1):393–405. CrossRefPubMedGoogle Scholar
  96. Steckel J, Peremans H (2013) BatSLAM: simultaneous localization and mapping using biomimetic sonar. PLoS ONE 8(1):e54076. CrossRefPubMedPubMedCentralGoogle Scholar
  97. Stone T, Differt D, Milford M, Webb B (2016) Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics. In: 2016 IEEE international conference on robotics and automation (ICRA). IEEE, pp 5615–5622.
  98. Tang G, Michmizos KP (2018) Gridbot: an autonomous robot controlled by a spiking neural network mimicking the brain’s navigational system. In: Proceedings of the international conference on neuromorphic systems, ACM. ACM Press.
  99. Tang H, Yan R, Tan KC (2018) Cognitive navigation by neuro-inspired localization, mapping, and episodic memory. IEEE Trans Cogn Dev Syst 10(3):751–761. CrossRefGoogle Scholar
  100. Taube J, Muller R, Ranck J (1990) Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J Neurosci 10(2):420–435. CrossRefPubMedPubMedCentralGoogle Scholar
  101. Thrun S, Leonard JJ (2008) Simultaneous localization and mapping. In: Springer Handbook of Robotics, Springer, Berlin, pp 871–889.
  102. Thrun S, Montemerlo M (2006) The graph SLAM algorithm with applications to large-scale mapping of urban structures. Int J Robot Res 25(5–6):403–429. CrossRefGoogle Scholar
  103. Vidal AR, Rebecq H, Horstschaefer T, Scaramuzza D (2018) Ultimate SLAM? Combining events, images, and IMU for robust visual SLAM in HDR and high-speed scenarios. IEEE Robot Autom Lett 3(2):994–1001. CrossRefGoogle Scholar
  104. Welchman AE (2016) The human brain in depth: How we see in 3D. Annu Rev Vis Sci 2(1):345–376. CrossRefPubMedGoogle Scholar
  105. Wohlgemuth MJ, Yu C, Moss CF (2018) 3D hippocampal place field dynamics in free-flying echolocating bats. Front Cell Neurosci 12:270. CrossRefPubMedPubMedCentralGoogle Scholar
  106. Yartsev MM, Ulanovsky N (2013) Representation of three-dimensional space in the hippocampus of flying bats. Science 340(6130):367–372. CrossRefPubMedGoogle Scholar
  107. Zeng T, Si B (2017) Cognitive mapping based on conjunctive representations of space and movement. Front Neurorobot 11:61. CrossRefPubMedPubMedCentralGoogle Scholar
  108. Zhang Z, Scaramuzza D (2018) A tutorial on quantitative trajectory evaluation for visual(-inertial) odometry. In: 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 7244–7251.
  109. Zhang Z, Rebecq H, Forster C, Scaramuzza D (2016) Benefit of large field-of-view cameras for visual odometry. In: 2016 IEEE international conference on robotics and automation (ICRA). IEEE, pp 801–808.
  110. Zhou X, Weber C, Wermter S (2018) A self-organizing method for robot navigation based on learned place and head-direction cells. In: 2018 International joint conference on neural networks (IJCNN). IEEE, pp 1–8.

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

Personalised recommendations