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.
Similar content being viewed by others
References
Arleo A, Gerstner W (2000) Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity. Biol Cybern 83(3):287–299. https://doi.org/10.1007/s004220000171
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. https://doi.org/10.1007/s10514-012-9317-9
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. https://doi.org/10.1038/s41586-018-0102-6
Barrera A, Weitzenfeld A (2008) Biologically-inspired robot spatial cognition based on rat neurophysiological studies. Auton Robots 25(1–2):147–169. https://doi.org/10.1007/s10514-007-9074-3
Behley J, Stachniss C (2018) Efficient surfel-based SLAM using 3D laser range data in urban environments. In: Robotics: science and systems. https://doi.org/10.15607/rss.2018.xiv.016
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. https://doi.org/10.1126/scirobotics.aar7650
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. https://doi.org/10.1073/pnas.1719054115
Burak Y, Fiete IR (2009) Accurate path integration in continuous attractor network models of grid cells. PLoS Comput Biol 5(2):e1000291. https://doi.org/10.1371/journal.pcbi.1000291
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. https://doi.org/10.1109/tro.2016.2624754
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. https://doi.org/10.1038/s41593-018-0189-y
Casali G, Bush D, Jeffery K (2019) Altered neural odometry in the vertical dimension. In: Proceedings of the national academy of sciences, p 201811867. https://doi.org/10.1073/pnas.1811867116
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. https://doi.org/10.1371/journal.pone.0172325
Cummins MJ, Newman P (2008) FAB-MAP: probabilistic localization and mapping in the space of appearance. Int J Robot Res 27(6):647–665. https://doi.org/10.1177/0278364908090961
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. https://doi.org/10.1109/tpami.2007.1049
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. https://doi.org/10.1109/70.938381
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. https://doi.org/10.1016/j.robot.2016.10.017
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. https://doi.org/10.1126/scirobotics.aau0307
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. https://doi.org/10.1109/tro.2013.2279412
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. https://doi.org/10.1007/978-3-319-10605-2-54
Engel J, Koltun V, Cremers D (2018) Direct sparse odometry. IEEE Trans Pattern Anal Mach Intell 40(3):611–625. https://doi.org/10.1109/tpami.2017.2658577
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. https://doi.org/10.1113/jp270666
Evers C, Naylor PA (2018) Acoustic SLAM. IEEE/ACM Trans Audio Speech Lang Process 26(9):1484–1498. https://doi.org/10.1109/taslp.2018.2828321
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. https://doi.org/10.1109/icra.2017.7989679
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. https://doi.org/10.1016/j.cell.2018.09.017
Finkelstein A, Las L, Ulanovsky N (2016) 3-D maps and compasses in the brain. Annu Rev Neurosci 39(1):171–96. https://doi.org/10.1146/annurev-neuro-070815-013831
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. https://doi.org/10.1038/s41467-018-05562-1
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. https://doi.org/10.1109/icra.2014.6906584
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. https://doi.org/10.1109/tro.2016.2623335
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. https://doi.org/10.1109/tpami.2017.2769655
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 2198–2204
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. https://doi.org/10.1242/jeb.186932
Geiger A, Ziegler J, Stiller C (2011) Stereoscan: dense 3D reconstruction in real-time. In: 2011 IEEE intelligent vehicles symposium (IV), pp 963–968. https://doi.org/10.1109/ivs.2011.5940405
Gianelli S, Harland B, Fellous JM (2018) A new rat-compatible robotic framework for spatial navigation behavioral experiments. J Neurosci Methods 294:40–50. https://doi.org/10.1016/j.jneumeth.2017.10.021
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. https://doi.org/10.1109/iros.2008.4650872
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. https://doi.org/10.1038/nature03721
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. https://doi.org/10.3389/fpsyg.2015.00925
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. https://doi.org/10.1177/0278364911434148
Horiuchi TK, Moss CF (2015) Grid cells in 3-D: reconciling data and models. Hippocampus 25(12):1489–1500. https://doi.org/10.1002/hipo.22469
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. https://doi.org/10.3389/fnbot.2015.00001
Jeffery KJ, Jovalekic A, Verriotis M, Hayman R (2013) Navigating in a three-dimensional world. Behav Brain Sci 36(05):523–543. https://doi.org/10.1017/s0140525x12002476
Jeffery KJ, Wilson JJ, Casali G, Hayman RM (2015) Neural encoding of large-scale three-dimensional space-properties and constraints. Front Psychol 6:927. https://doi.org/10.3389/fpsyg.2015.00927
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. https://doi.org/10.1113/jp272945
Karrer M, Schmuck P, Chli M (2018) CVI-SLAM—collaborative visual-inertial SLAM. IEEE Robot Autom Lett 3(4):2762–2769. https://doi.org/10.1109/lra.2018.2837226
Kim M, Maguire EA (2018a) Encoding of 3D head direction information in the human brain. Hippocampus 29:619–629. https://doi.org/10.1002/hipo.23060
Kim M, Maguire EA (2018b) Hippocampus, retrosplenial and parahippocampal cortices encode multicompartment 3D space in a hierarchical manner. Cereb Cortex 28(5):1898–1909. https://doi.org/10.1093/cercor/bhy054
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. https://doi.org/10.1016/j.neuroimage.2018.11.041
Kim M, Jeffery KJ, Maguire EA (2017) Multivoxel pattern analysis reveals 3D place information in the human hippocampus. J Neurosci 37(16):4270–4279. https://doi.org/10.1523/jneurosci.2703-16.2017
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. https://doi.org/10.1109/ismar.2007.4538852
Konolige K, Agrawal M (2008) FrameSLAM: from bundle adjustment to real-time visual mapping. IEEE Trans Robot 24(5):1066–1077. https://doi.org/10.1109/tro.2008.2004832
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. https://doi.org/10.1109/iscas.2018.8351509
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. https://doi.org/10.1109/IROS.2018.8594228
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. https://doi.org/10.1016/j.robot.2018.08.002
Kropff E, Carmichael JE, Moser MB, Moser EI (2015) Speed cells in the medial entorhinal cortex. Nature 523(7561):419–424. https://doi.org/10.1038/nature14622
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. https://doi.org/10.1016/j.neuron.2017.12.020
Laurens J, Kim B, Dickman JD, Angelaki DE (2016) Gravity orientation tuning in macaque anterior thalamus. Nat Neurosci 19(12):1566–1568. https://doi.org/10.1038/nn.4423
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. https://doi.org/10.1523/jneurosci.1319-09.2009
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. https://doi.org/10.1016/j.neunet.2015.09.006
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. https://doi.org/10.1109/tro.2015.2496823
Lynen S, Bosse M, Siegwart R (2016) Keyframe-based visual–inertial odometry using nonlinear optimization. Int J Robot Res 124(1):49–64. https://doi.org/10.1007/s11263-016-0947-9
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. https://doi.org/10.1177/0278364912438273
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. https://doi.org/10.1109/lra.2018.2855443
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. https://doi.org/10.1038/nrn1932
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. https://doi.org/10.1016/j.robot.2004.09.018
Milford M (2013) Vision-based place recognition: How low can you go? Int J Robot Res 32(7):766–789. https://doi.org/10.1177/0278364913490323
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. https://doi.org/10.1098/rstb.2013.0484
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. https://doi.org/10.1109/tro.2008.2004520
Milford M, Wyeth G (2010) Persistent navigation and mapping using a biologically inspired SLAM system. Int J Robot Res 29(9):1131–1153. https://doi.org/10.1016/j.robot.2010.05.004
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. https://doi.org/10.1109/icra.2012.6224623
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. https://doi.org/10.1109/robot.2004.1307183
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. https://doi.org/10.15607/rss.2013.ix.003
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. https://doi.org/10.1109/icra.2011.5980329
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)
Moser EI, Moser MB, McNaughton BL (2017) Spatial representation in the hippocampal formation: a history. Nat Neurosci 20(11):1448–1464. https://doi.org/10.1038/nn.4653
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. https://doi.org/10.3389/fncom.2016.00013
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. https://doi.org/10.1109/tro.2017.2705103
Mur-Artal R, Montiel JMM, Tardos JD (2015) ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans Robot 31(5):1147–1163. https://doi.org/10.1109/tro.2015.2463671
Naseer T, Burgard W, Stachniss C (2018) Robust visual localization across seasons. IEEE Trans Robot 34(2):289–302. https://doi.org/10.1109/tro.2017.2788045
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. https://doi.org/10.1109/iccv.2011.6126513
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. https://doi.org/10.1016/0006-8993(71)90358-1
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. https://doi.org/10.1152/jn.00501.2017
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. https://doi.org/10.1109/robot.2010.5509587
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. https://doi.org/10.1109/tro.2018.2853729
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. https://doi.org/10.1109/lra.2016.2645143
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. https://doi.org/10.2514/6.2016-0404
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. https://doi.org/10.1109/ijcnn.2017.7966383
Samsonovich A, McNaughton BL (1997) Path integration and cognitive mapping in a continuous attractor neural network model. J Neurosci 17(15):5900–5920. https://doi.org/10.1523/jneurosci.17-15-05900.1997
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. https://doi.org/10.1145/3177853
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):1418–1425
Shinder ME, Taube JS (2019) Three-dimensional tuning of head direction cells in rats. J Neurophysiol 121(1):4–37. https://doi.org/10.1152/jn.00880.2017
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. https://doi.org/10.1113/jp270630
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. https://doi.org/10.1109/icar.2013.6766531
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. https://doi.org/10.1016/j.ifacol.2015.06.035
Solstad T, Boccara CN, Kropff E, Moser MB, Moser EI (2008) Representation of geometric borders in the entorhinal cortex. Science 322(5909):1865–1868. https://doi.org/10.1126/science.1166466
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. https://doi.org/10.1038/s41467-018-06441-5
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. https://doi.org/10.1152/jn.2000.83.1.393
Steckel J, Peremans H (2013) BatSLAM: simultaneous localization and mapping using biomimetic sonar. PLoS ONE 8(1):e54076. https://doi.org/10.1371/journal.pone.0054076
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. https://doi.org/10.1109/icra.2016.7487780
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. https://doi.org/10.1145/3229884.3229888
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. https://doi.org/10.1109/tcds.2017.2776965
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. https://doi.org/10.1523/jneurosci.10-02-00420.1990
Thrun S, Leonard JJ (2008) Simultaneous localization and mapping. In: Springer Handbook of Robotics, Springer, Berlin, pp 871–889. https://doi.org/10.1007/978-3-540-30301-5-38
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. https://doi.org/10.1177/0278364906065387
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. https://doi.org/10.1109/lra.2018.2793357
Welchman AE (2016) The human brain in depth: How we see in 3D. Annu Rev Vis Sci 2(1):345–376. https://doi.org/10.1146/annurev-vision-111815-114605
Wohlgemuth MJ, Yu C, Moss CF (2018) 3D hippocampal place field dynamics in free-flying echolocating bats. Front Cell Neurosci 12:270. https://doi.org/10.3389/fncel.2018.00270
Yartsev MM, Ulanovsky N (2013) Representation of three-dimensional space in the hippocampus of flying bats. Science 340(6130):367–372. https://doi.org/10.1126/science.1235338
Zeng T, Si B (2017) Cognitive mapping based on conjunctive representations of space and movement. Front Neurorobot 11:61. https://doi.org/10.3389/fnbot.2017.00061
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. https://doi.org/10.1109/IROS.2018.8593941
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. https://doi.org/10.1109/icra.2016.7487210
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. https://doi.org/10.1109/ijcnn.2018.8489348
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Benjamin Lindner.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Yu, F., Shang, J., Hu, Y. et al. NeuroSLAM: a brain-inspired SLAM system for 3D environments. Biol Cybern 113, 515–545 (2019). https://doi.org/10.1007/s00422-019-00806-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00422-019-00806-9