Abstract
This paper proposes an implementation and evaluation in a real-world environment of a new bio-inspired predictive navigation model for mobility control, suitable especially for visually impaired people. This model relies on the interactions between formal models of three types of neurons identified in the mammals’ brain implied in navigation tasks (namely place cells, grid cells, and head direction cells) to construct a topological model of the environment under the form of a decentralized navigation graph. The proposed model, previously tested in virtual environments, demonstrated a high tolerance to motion drift and robustness to environment changes. This paper presents an implementation of this navigation model, based on a stereoscopic camera, and evaluates its possibilities to map and guide a person in an unknown real environment. The evaluation results confirm the effectiveness of the proposed bio-inspired navigation model to build a path map and guide a person through this path, while remaining robust to environment changes, and estimating traveled distances with an error rate below 2% over test paths, up to 100 m. These results open the way toward efficient wearable assistive devices for visually impaired people navigation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
APH (American Printing House). https://tech.aph.org/neios/. Accessed 15 Nov 2022
Seeing Eye GPS, Sendero Group. http://www.senderogroup.com/products/SeeingEyeGPS/. Accessed 15 Nov 2022
Hengle, A., Kulkarni, A., Bavadekar, N., Kulkarni, N., Udyawar,R.: Smart cap: a deep learning and IoT based assistant for the visually impaired. In: 3rd International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 1109–1116 (2020)
Google Map. https://www.google.com/maps. Accessed 15 Nov 2022
Flores, G., Manduchi, R.: Easy return: an App for indoor backtracking assistance. In: ACM CHI 2018, pp. 1–12, USA (2018)
Fallah, N., Apostolopoulos, I., Bekris, K., Folmer, E.: Indoor human navigation systems: a survey. Interact. Comput. 25(1), 21–33 (2013)
Liu, K., Motta, G., Dong, J., Hashish I.A.: Wi-Fi-aided magnetic field positioning with floor estimation in indoor multi-floor navigation services. In: ICIOT, Honolulu (2017)
Fusco, G., Coughlan, J.M.: Indoor localization for visually impaired travelers using computer vision on a smartphone. In: Proceedings of the ACM Web4All Conference: Automation for Accessibility, Taiwan (2020)
Campos, C., Elvira, R., Gómez Rodríguez, J.J., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM3: an accurate open-source library for visual, visual-inertial and multi-map SLAM. IEEE Trans. Rob. 37(6), 1874–1890 (2021)
O’Keefe, J., Nadel, L.: The Hippocampus as a Cognitive Map. Clarendon Press, Oxford (1978)
Hafting, T., Fyhn, M., Molden, S., Moser, M.B., Moser, E.I.: Microstructure of a spatial map in the entorhinal cortex. Nature 436(7052), 801–806 (2005)
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, 420–435 (1990)
Gaussier, P., et al.: Merging information in the entorhinal cortex: what can we learn from robotics experiments and modeling? J. Exp. Biol. 222, 1–13 (2019)
Zhou, X., Weber, C., Wermter, S.: A self-organizing method for robot navigation based on learned place and head-direction cells. In: Proceedings of the International Joint Conference Neural Networks, vol. 2018, pp. 1–8 (2018)
Zhou, X., Bai, T., Gao, Y., Han, Y.: Vision-based robot navigation through combining unsupervised learning and hierarchical reinforcement learning. Sensors 19(7), 1–23 (2019)
Chen, Q., Mo, H.: A brain-inspired goal-oriented robot navigation system. Appl. Sci. 9(22), 4869 (2019)
Karaouzene, A., Delarboulas, P., Vidal, D., Gaussier, P., Quoy, M., Ramesh, C.: Social interaction for object recognition and tracking. In: IEEE ROMAN Workshop on Developmental and Bio-Inspired Approaches for Social Cognitive Robotics (2013)
Milford, M., Wyeth, G.: Persistent navigation and mapping using a biologically inspired slam system. Int. J. Rob. Res. 29(9), 1131–1153 (2010)
Milford, M.J., Wyeth, G.F.: SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights. In: Proceedings of the IEEE International Conference Robotics Automation, pp. 1643–1649 (2012)
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)
Gay, S.L., Le Run, K., Pissaloux, E., Romeo, K., Lecomte, C.: Towards a predictive bio-inspired navigation model. J. Inf. 12(3), 1–19 (2021)
Pissaloux, E., Velazquez, R., Maingreaud, F.: A new framework for cognitive mobility of visually impaired users and associated tactile device. IEEE T-HMS Trans. Hum.-Mach. Syst. 47(6), 2168–2291 (2017)
Stensola, H., Stensola, T., Solstad, T., FrØland, K., Moser, M.B., Moser, E.I.: The entorhinal grid map is discretized. Nature 492(7427), 72–78 (2012)
sieuwe elferink github repository. https://github.com/sieuwe1/PS4-eye-camera-for-linux-with-python-and-OpenCV. Accessed 15 Nov 2022
Acknowledgements
This work is supported by the French National Research Agency (ANR) in the frameworks of “Investissements d’avenir” (ANR-15-IDEX-02) and “Inclusive Museum Guide” (IMG, ANR-20-CE38-0007), and by the Region of Normandy and European Commission in the frame of “Guide Muséal”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gay, S.L., Pissaloux, E., Jamont, JP. (2023). A Bio-Inspired Model for Robust Navigation Assistive Devices: A Proof of Concept. In: Papadopoulos, G.A., Achilleos, A., Pissaloux, E., Velázquez, R. (eds) ICT for Health, Accessibility and Wellbeing. IHAW 2022. Communications in Computer and Information Science, vol 1799. Springer, Cham. https://doi.org/10.1007/978-3-031-29548-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-29548-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29547-8
Online ISBN: 978-3-031-29548-5
eBook Packages: Computer ScienceComputer Science (R0)