@inproceedings{bf538305e68f4d9e8072319e73b46b5d,
title = "Intensity histogram based segmentation of 3D point cloud using Growing Neural Gas",
abstract = "This paper proposes a 3D point cloud segmentation method using a reflection intensity of Laser Range Finder (LRF). In this paper, we use LRF and tilt unit for acquiring a 3D point cloud. First of all, we apply Growing Neural Gas (GNG) to the point cloud for learning a topological structure of the point cloud. Next, we proposed a segmentation method based on an intensity histogram that is composed of the nearest data of each node. Finally, we show experimental results of the proposed method and discuss the effectiveness of the proposed method.",
keywords = "Clustering, LRF intensity, Robot sensing",
author = "Shin Miyake and Yuichiro Toda and Naoyuki Kubota and Naoyuki Takesue and Kazuyoshi Wada",
note = "Funding Information: This work was funded by ImPACT Program of the Council for Science, Technology and Innovation. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 9th International Conference on Intelligent Robotics and Applications, ICIRA 2016 ; Conference date: 22-08-2016 Through 24-08-2016",
year = "2016",
doi = "10.1007/978-3-319-43518-3_33",
language = "English",
isbn = "9783319435176",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "335--345",
editor = "Kazuo Kiguchi and Naoyuki Kubota and Takenori Obo and Honghai Liu",
booktitle = "Intelligent Robotics and Applications - 9th International Conference, ICIRA 2016, Proceedings",
}