Abstract
Environmental map building is an important premise of robot navigation, Self-Organizing Map (SOM) or Growing Self-Organizing Map (GSOM) can establish the topological map of the indoor environment through the environmental cognition. Aimed at the problem of traditional SOM containing error structure in constructing topological map while long time consuming of GSOM, this paper proposes a Prunable Self-Organizing Map (PSOM) algorithm which can prune wrong connections between neurons from the environmental structure, thus proving accurate topology while greatly reducing the implementation cost of the program compared to the Growing Self-Organizing Map. The proposed method (PSOM) is verified by experimental of environment map building based on physical indoor environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Thrun, S.: Learning metric-topological maps for indoor mobile robot navigation. Artif. Intell. 99(1), 21–71 (1998)
Yan, Z., Xiaodong, X., Wei, W.: Mobile robot geometrictopological map building and selflocalization. Control Decis. 20(7), 815–818 (2015)
Xiaogang, R., Xutao, X., Xinyuan, L.: A desktop robot system and its autonomous mapping algorithm. Control Eng. 17(6), 856–860 (2010)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)
Vlassis, N.A., Papakonstantinou, G., Tsanakas, P.: Robot Map Building by Kohonen’s Self-Organizing Neural Networks (1998)
Fritzke, B.: Growing cell structures — a self-organizing network for unsupervised and supervised learning. Neural Netw. 7(9), 1411–1460 (1994)
Choi, D.I., Park, S.H.: Self-creating and organizing neural networks. IEEE Trans. Neural Netw. 5(4), 561–575 (1994)
Fritzke, B.: A growing neural gas network learns topologies. In: Advances in Neural Information Processing Systems, pp. 625–632. MIT Press, Cambridge, USA (1995)
Xiaogang, R., Shaomin, X., Xinyuan, L.: Mapping based on the growing self-organizing map (GSOM). J. Syst. Simul. 20(1), 81–84 (2008)
Yan, W., Weber, C., Wermter, S.: A neural approach for robot navigation based on cognitive map learning. In: International Joint Conference on Neural Networks, pp. 1–8. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, C., Ruan, Xg., Gu, K., Zhu, Xq. (2018). Construction of an Indoor Topological Map of a Robot Based on Prunable Self-Organizing Map. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_44
Download citation
DOI: https://doi.org/10.1007/978-981-10-8108-8_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8107-1
Online ISBN: 978-981-10-8108-8
eBook Packages: Computer ScienceComputer Science (R0)