Abstract
This paper proposes a real-time topological structure learning method based on concentrated/distributed sensing for a 2D/3D point cloud. First of all, we explain a modified Growing Neural Gas with Utility (GNG-U2) that can learn the topological structure of 3D space environment and color information simultaneously by using a weight vector. Next, we propose a Region Of Interest Growing Neural Gas (ROI-GNG) for realizing concentrated/distributed sensing in real-time. In ROI-GNG, the discount rates of the accumulated error and utility value are variable according to the situation. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.
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
References
Murphy, R.R., et al.: Search and rescue robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1151–1173. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-30301-5_51
Toda, Y., Yu, H., Ju, Z., Takesue, N., Wada, K., Kubota, N.: Real-time 3D point cloud segmentation using growing neural gas with utility. In: The 9th International Conference on Human System Interaction, pp. 418–422 (2016)
Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 613–618. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0020222
Fritzke, B.: A growing neural gas network learns topologies. Adv. Neural. Inf. Process. Syst. 7, 625–632 (1995)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (2000)
Martinetz, T.M., Schulten, K.J.: A “neural-gas’’’ network learns topologies. Artif. Neural Netw. 1, 397–402 (1991)
Fritzke, B.: Unsupervised clustering with growing cell structures. Neural Netw. 2, 531–536 (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Toda, Y., Li, X., Matsuno, T., Minami, M. (2019). Region of Interest Growing Neural Gas for Real-Time Point Cloud Processing. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11742. Springer, Cham. https://doi.org/10.1007/978-3-030-27535-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-27535-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27534-1
Online ISBN: 978-3-030-27535-8
eBook Packages: Computer ScienceComputer Science (R0)