Abstract
To successfully perform autonomous navigation, mobile agents must solve the Simultaneous Localization and Mapping (SLAM) problem. However, acquiring the map in a single SLAM session may not be possible, thus the map may be incrementally built over multiple sessions. Two solutions could be considered to solve the multisession SLAM problem: (i) the robot must localize itself in the previously stored map before the new session starts; (ii) it can start a new map and merge it with the map from the previous sessions. To date, only scenario (i) has been addressed by RatSLAM, an algorithm inspired by the navigation system in rodent brains. Therefore, this work proposes a multisession solution that solves both scenarios. A new mechanism merges the data from the RatSLAM structures of the current mapping session with those previously stored if there are connections between these paths. This approach was tested in four different scenarios, from virtual controlled environments to real-world environments with two, three, and five sessions. The robot started in an unfamiliar location for each mapping session, but it also works if the agent starts in a known place, scenario (ii) and (i), respectively. For all experiments, the entire map was consistently obtained. Furthermore, the proposed approach updates and enhances the previous session’s map in real-world environments. Therefore, the proposed approach may be a multiple SLAM session solution for the RatSLAM algorithm.
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Data Availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Code Availability
The code that supports the findings of this study is available at https://zenodo.org/badge/latestdoi/568248424.
Change history
26 October 2023
Missing Open Access funding information has been added in the Funding Note.
References
Pandey, A., Pandey, S., Parhi, D.: Mobile robot navigation and obstacle avoidance techniques: a review. Int. Rob. Auto. J. 2(3), 105 (2017). https://doi.org/10.15406/iratj.2017.02.00023
Labbé, M., Michaud, F.: Online global loop closure detection for large-scale multi-session graph-based slam. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2661–2666 (2014). https://doi.org/10.1109/IROS.2014.6942926
Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part i. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006). https://doi.org/10.1109/MRA.2006.1638022
Stachniss, C., Leonard, J.J., Thrun, S.: Simultaneous localization and mapping. In: Springer Handbook of Robotics, (2016). https://doi.org/10.1007/978-3-319-32552-1_46
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B., et al.: Fastslam: A factored solution to the simultaneous localization and mapping problem. Aaai/iaai 593598 (2002)
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007). https://doi.org/10.1109/TRO.2006.889486
Silva, G., Costa, J., Magalhães, T., Reis, L.P.: Cyberrescue: A pheromone approach to multi-agent rescue simulations. In: Information Systems and Technologies (CISTI), 2010 5th Iberian Conference On, pp. 1–6 (2010). IEEE
Bakhshipour, M., Jabbari Ghadi, M., Namdari, F.: Swarm robotics search & rescue: a novel artificial intelligence-inspired optimization approach. Appl. Soft. Comput. 57, 708–726 (2017). https://doi.org/10.1016/j.asoc.2017.02.028
Cai, Y., Chen, Z., Min, H.: Improving particle swarm optimization algorithm for distributed sensing and search. In: 2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 373–379 (2013). https://doi.org/10.1109/3PGCIC.2013.64
Ranjbar-Sahraei, B., Tuyls, K., Caliskanelli, I., Broeker, B., Claes, D., Alers, S., Weiss, G.: 13 - bio-inspired multi-robot systems. In: Ngo, T.D. (ed.) Biomimetic Technologies. Woodhead Publishing Series in Electronic and Optical Materials, pp. 273–299 (2015). https://doi.org/10.1016/B978-0-08-100249-0.00013-6. https://www.sciencedirect.com/science/article/pii/B9780081002490000136
Calvo, R., Oliveira, J.R.d., Figueiredo, M., Francelin Romero, R.A.: A distributed, bio-inspired coordination strategy for multiple agent systems applied to surveillance tasks in unknown environments. In: The 2011 International Joint Conference on Neural Networks, pp. 3248–3255 (2011). https://doi.org/10.1109/IJCNN.2011.6033652
Zeno, P.J., Patel, S., Sobh, T.M.: Review of neurobiologically based mobile robot navigation system research performed since 2000. J. Robot. 2016, 17 (2016). https://doi.org/10.1155/2016/8637251
Tang, H., Yan, R., Tan, K.C.: Cognitive Navigation by Neuro-Inspired Localization, Mapping, and Episodic Memory. IEEE Trans. Cogn. Dev. Syst. 10(3), 751–761 (2018). https://doi.org/10.1109/TCDS.2017.2776965
Yu, F., Shang, J., Hu, Y., Milford, M.: NeuroSLAM: a brain-inspired SLAM system for 3D environments. Biol. Cybern. (2019). https://doi.org/10.1007/s00422-019-00806-9
Li, J., Li, Z., Chen, F., Bicchi, A., Sun, Y., Fukuda, T.: Combined sensing, cognition, learning, and control for developing future neuro-robotics systems: a survey. IEEE Trans. Cogn. Dev. Syst. 11(2), 148–161 (2019). https://doi.org/10.1109/TCDS.2019.2897618
McNaughton, B.L., Battaglia, F.P., Jensen, O., Moser, E.I., Moser, M.-B.: Path integration and the neural basis of the’cognitive map’. Nat. Rev. Neurosci. 7(8), 663 (2006). https://doi.org/10.1038/nrn1932
O’Keefe, J.: Place units in the hippocampus of the freely moving rat. Exp. Neurol. 51(1), 78–109 (1976). https://doi.org/10.1016/0014-4886(76)90055-8
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., Moser, E.I.: Microstructure of a spatial map in the entorhinal cortex. Nature 436(7052), 801 (2005). https://doi.org/10.1038/nature03721
Taube, J., Muller, R., Ranck, J.: Head-direction cells recorded from the postsubiculum in freely moving rats. i. description and quantitative analysis. J. NeuroSci. 10(2), 420–435 (1990). https://doi.org/10.1523/JNEUROSCI.10-02-00420.1990
Milford, M.J., Wyeth, G.F., Prasser, D.: Ratslam: a hippocampal model for simultaneous localization and mapping. In: IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ’04. 2004, vol. 1, pp. 403–4081. https://doi.org/10.1109/ROBOT.2004.1307183 (2004)
Ball, D., Heath, S., Wiles, J., Wyeth, G., Corke, P., Milford, M.: Openratslam: an open source brain-based slam system. Auton. Robot. 34(3), 149–176 (2013). https://doi.org/10.1007/s10514-012-9317-9
Milford, M., Jacobson, A., Chen, Z., Wyeth, G.: RatSLAM: Using models of rodent hippocampus for robot navigation and beyond. In: Springer Tracts in Advanced Robotics, vol. 114, pp. 467–485 (2016). https://doi.org/10.1007/978-3-319-28872-7_27
Prasser, D., Milford, M., Wyeth, G.: Outdoor simultaneous localisation and mapping using ratslam. In: Corke, P., Sukkariah, S. (eds.) Field and Service Robotics, pp. 143–154. Springer (2006). https://doi.org/10.1007/978-3-540-33453-8_13
Milford, M.J., Wyeth, G.F.: Mapping a suburb with a single camera using a biologically inspired slam system. IEEE Trans. Robot. 24(5), 1038–1053 (2008). https://doi.org/10.1109/TRO.2008.2004520
Milford, M., Wyeth, G.: Persistent navigation and mapping using a biologically inspired slam system. Int. J. Robot. Res. 29(9), 1131–1153 (2010). https://doi.org/10.1177/0278364909340592
Menezes, M.C., de Freitas, E.P., Cheng, S., de Oliveira, A.C.M., de Almeida Ribeiro, P.R.: A neuro-inspired approach to solve a simultaneous location and mapping task using shared information in multiple robots systems. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1753–1758 (2018). https://doi.org/10.1109/ICARCV.2018.8581270
McDonald, J., Kaess, M., Cadena, C., Neira, J., Leonard, J.J.: Real-time 6-DOF multi-session visual SLAM over large-scale environments. Robot. Auton. Syst. 61(10), 1144–1158 (2013). https://doi.org/10.1016/j.robot.2012.08.008
Labbé, M., Michaud, F.: Long-term online multi-session graph-based SPLAM with memory management 42, 1133–1150 (2018). https://doi.org/10.1007/s10514-017-9682-5
Labbé, M., Michaud, F.: Online global loop closure detection for large-scale multi-session graph-based slam. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2661–2666 (2014). https://doi.org/10.1109/IROS.2014.6942926
Campos, C., Elvira, R., Rodríguez, J.J.G., Montiel, J.M., Tardós, J.D.: Orb-slam3: An accurate open-source library for visual, visual-inertial, and multimap slam. IEEE Trans. Robot. 37(6), 1874–1890 (2021)
Daoud, H.A., Sabri, A.Q.M., Loo, C.K., Mansoor, A.M.: Slamm: Visual monocular slam with continuous mapping using multiple maps. PloS one 13(4), 0195878 (2018)
Schneider, T., Dymczyk, M., Fehr, M., Egger, K., Lynen, S., Gilitschenski, I., Siegwart, R.: maplab: An open framework for research in visual-inertial mapping and localization. IEEE Robot. Autom. Lett. 3(3), 1418–1425 (2018)
McDonald, J., Kaess, M., Cadena, C., Neira, J., Leonard, J.J.: Real-time 6-dof multi-session visual slam over large-scale environments. Robot. Auton. Syst. 61(10), 1144–1158 (2013)
Wang, Y., Huang, S., Xiong, R., Wu, J.: A framework for multi-session rgbd slam in low dynamic workspace environment. CAAI Trans Intell Technol 1(1), 90–103 (2016)
Burguera Burguera, A., Bonin-Font, F.: A trajectory-based approach to multi-session underwater visual slam using global image signatures. J Marine Science and Engineering 7(8), 278 (2019)
Labbé, M., Michaud, F.: Multi-session visual slam for illumination invariant localization in indoor environments (2021). arXiv:2103.03827
Milford, M.J., Wiles, J., Wyeth, G.F.: Solving navigational uncertainty using grid cells on robots. PLOS Computational Biology 6(11), 1–14 (2010). https://doi.org/10.1371/journal.pcbi.1000995
Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Schenker, P.S. (ed.) Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586–606. International Society for Optics and Photonics (1992). https://doi.org/10.1117/12.57955
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361, IEEE (2012)
Walther, T., Diekmann, N., Vijayabaskaran, S., Donoso, J.R., Manahan-Vaughan, D., Wiskott, L., Cheng, S.: Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach. Scientific Reports 11(1) (2021). https://doi.org/10.1038/s41598-021-81157-z
Smith, M., Baldwin, I., Churchill, W., Paul, R., Newman, P.: The new college vision and laser data set. The International Journal of Robotics Research 28(5), 595–599 (2009)
Acknowledgements
Our research group acknowledges financial support from FAPEMA/COOPI COOPI-05109/18, CAPES/BRAZIL Finance Code 001 and CNPq/BRAZIL (Projects 309505/2020-8 and 420109/2018-8). Sen Cheng was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) - project number 316803389 - SFB 1280, A14. Edison Pignaton de Freitas also thanks to the Ruhr-Universität Bochum - Research School PLUS, for the Visiting International Professor (VIP) grant.
Funding
Open Access funding provided by the IReL Consortium. Financial support from FAPEMA/COOPI COOPI-05109/18, CAPES/BRAZIL (Finance Code 001), CNPq projects 309505/2020-8 and 420109/2018-8, and German Research Foundation (DFG) - project number 316803389 - SFB 1280, A14. Edison Pignaton de Freitas also thanks to the Ruhr-Universität Bochum - Research School PLUS, for the Visiting International Professor (VIP) grant. Alexandre Oliveira also thanks to the Science Foundation Ireland (SFI) grant SFI/16/RC/3918 (CONFIRM) and Marie Sklodowska-Curie agreement 847.577 co-funded by the European Regional Development Fund.
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Matheus: Method conception/design, implementation and tests. Wrote original draft, revised and edited the manuscript. Mauro: Method conception/design. Revised and edited the manuscript. Edison: Revised and edited the manuscript. Sen Cheng: Revised and edited the manuscript. Areolino: Revised and edited the manuscript. Paulo: Wrote original draft, revised and edited the manuscript. Alexandre: Wrote original draft, revised and edited the manuscript.
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Menezes, M., Muñoz, M., Pignaton de Freitas, E. et al. A Multisession SLAM Approach for RatSLAM. J Intell Robot Syst 108, 61 (2023). https://doi.org/10.1007/s10846-023-01816-3
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DOI: https://doi.org/10.1007/s10846-023-01816-3