Abstract
The Fukushima nuclear power plant accident that occurred in 2011 eastern Japan has created local spots with a high dose level in residential areas in eastern Japan. It is necessary to build a map of the distribution of radiation activities that shows the locations and intensities of radiation sources to assist in decontamination work. The proposed method estimates the intensities of point-like sources from the radiation dose measured by moving a dosimeter in three dimensions and 3D map of the environment, assuming that the radioactive sources are located on the ground surface. The radiation dose is measured as a few counts including a wide variability because in the proposed method, low levels of radiation are measured by moving a dosimeter. For estimating the intensities, the maximum a posteriori probability (MAP) estimation is computed on the basis of the radiation characteristics of attenuation and stochastic counts. Experiments of estimation in real environments were conducted. The experimental results showed that using the proposed method, it is possible to estimate the distributions of radiation sources using the radiation dose measured by a dosimeter and the measured 3D shape of the ground and building surface.
Original language | English |
---|---|
Title of host publication | IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1889-1895 |
Number of pages | 7 |
ISBN (Electronic) | 9781479969340 |
DOIs | |
Publication status | Published - 2014 Jan 1 |
Event | 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States Duration: 2014 Sep 14 → 2014 Sep 18 |
Other
Other | 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 |
---|---|
Country/Territory | United States |
City | Chicago |
Period | 14/9/14 → 14/9/18 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications