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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/13709

タイトル: Fast Radiation Mapping and Unknown Multiple Sources Localization using Topographic Contour Map and Incremental Density Estimation
著者: Newaz, Abdullah Al Redwan
Jeong, Sungmoon
LEE, Hosun
Ryu, Hyejeong
Chong, Nak Young
Mason, Matthew T.
キーワード: unmanned aerial vehicles
radiation mapping
Gaussian mixture model
incremental learning
発行日: 2016-05-16
出版者: IEEE
誌名: 2016 IEEE International Conference on Robotics and Automation (ICRA)
開始ページ: 1515
終了ページ: 1521
DOI: 10.1109/ICRA.2016.7487288
抄録: Toward a global picture of the radiation exposure of an area, particularly for fast emergency response, a UAV based exploration method is proposed. Without a priori knowledge of the radiation field, it is difficult to select the region of interest (ROI) which includes all radiation sources. For the case of a single radiation source, a greedy algorithm may localize the source by finding the maximum radiation value. However, when multiple sources generate a hotspot in a cumulative manner, the hotspot position does not coincide with one of the source positions. Therefore, we propose an efficient exploration method to quickly localize the radiation sources using the following procedures: (1) ROI selection using topographic maps with specific radiation level selection methods and (2) source localization estimating the number of sources and their positions with incremental variational Bayes inference of Gaussian mixtures. Under three different conditions according to the number of sources and their positions, we have shown that the proposed model can reduce the ROI and significantly improve the estimation accuracy than existing methods.
Rights: Copyright (C) 2016 IEEE. Abdullah Al Redwan Newaz, Sungmoon Jeong, Hosun LEE, Hyejeong Ryu, Nak Young Chong, Matthew T. Mason, 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, 1515-1521. http://dx.doi.org/10.1109/ICRA.2016.7487288
URI: http://hdl.handle.net/10119/13709
資料タイプ: author
出現コレクション:b11-1. 会議発表論文・発表資料 (Conference Papers)


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