Gamma-Ray Imaging with Spatially Continuous Intensity Statistics
(2021) 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems p.5234-5239- Abstract
- Novel methods for the inference of radiation intensity
functions defined over known surfaces are proposed, intended
for use in surveying applications with mobile spectrometers.
Previous approaches, based on the maximum likelihood
expectation maximization (ML-EM) framework with Poisson
likelihoods, are extended to better handle spatially continuous
intensity statistics using ideas from Gaussian filtering. The
resulting algorithm is evaluated against a classical ML-EM
method, and a recently proposed sparse additive point source
localization (APSL) algorithm in a Monte-Carlo simulation
study. The new generalized ASPL (GASPL) is shown to
compare favorably in terms of estimation accuracy when... (More) - Novel methods for the inference of radiation intensity
functions defined over known surfaces are proposed, intended
for use in surveying applications with mobile spectrometers.
Previous approaches, based on the maximum likelihood
expectation maximization (ML-EM) framework with Poisson
likelihoods, are extended to better handle spatially continuous
intensity statistics using ideas from Gaussian filtering. The
resulting algorithm is evaluated against a classical ML-EM
method, and a recently proposed sparse additive point source
localization (APSL) algorithm in a Monte-Carlo simulation
study. The new generalized ASPL (GASPL) is shown to
compare favorably in terms of estimation accuracy when the
true intensity is not well described by a set of point sources.
Finally, the GASPL is used in an experiment where a detector is
mounted to an unmanned aerial vehicle to estimate the intensity
and location of radioactive sources placed in a meadow. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/fc938269-6644-470f-88b3-9f9cf73c93f6
- author
- Greiff, Marcus LU ; Rofors, Emil LU ; Robertsson, Anders LU ; Johansson, Rolf LU and Tyllström, Rikard LU
- organization
- publishing date
- 2021-09
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proc. 2021 IEEE/RSJ Int. Conf.Intelligent Robots and Systems (IROS2021), Sep 27 - Oct 1, 2021, Prague, Czech Republic
- pages
- 6 pages
- conference name
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
- conference location
- Prague, Czech Republic
- conference dates
- 2021-09-27 - 2021-10-01
- project
- THE FUTURE OF DRONES: TECHNOLOGIES, APPLICATIONS, RISKS AND ETHICS
- UAS@LU: Autonomous Flight
- RobotLab LTH
- ELLIIT LU P06: Collaborative Robotic Systems
- Semantic Mapping and Visual Navigation for Smart Robots
- language
- English
- LU publication?
- yes
- id
- fc938269-6644-470f-88b3-9f9cf73c93f6
- date added to LUP
- 2021-11-19 22:07:08
- date last changed
- 2021-12-03 02:18:10
@inproceedings{fc938269-6644-470f-88b3-9f9cf73c93f6, abstract = {{Novel methods for the inference of radiation intensity<br/>functions defined over known surfaces are proposed, intended<br/>for use in surveying applications with mobile spectrometers.<br/>Previous approaches, based on the maximum likelihood<br/>expectation maximization (ML-EM) framework with Poisson<br/>likelihoods, are extended to better handle spatially continuous<br/>intensity statistics using ideas from Gaussian filtering. The<br/>resulting algorithm is evaluated against a classical ML-EM<br/>method, and a recently proposed sparse additive point source<br/>localization (APSL) algorithm in a Monte-Carlo simulation<br/>study. The new generalized ASPL (GASPL) is shown to<br/>compare favorably in terms of estimation accuracy when the<br/>true intensity is not well described by a set of point sources.<br/>Finally, the GASPL is used in an experiment where a detector is<br/>mounted to an unmanned aerial vehicle to estimate the intensity<br/>and location of radioactive sources placed in a meadow.}}, author = {{Greiff, Marcus and Rofors, Emil and Robertsson, Anders and Johansson, Rolf and Tyllström, Rikard}}, booktitle = {{Proc. 2021 IEEE/RSJ Int. Conf.Intelligent Robots and Systems (IROS2021), Sep 27 - Oct 1, 2021, Prague, Czech Republic}}, language = {{eng}}, pages = {{5234--5239}}, title = {{Gamma-Ray Imaging with Spatially Continuous Intensity Statistics}}, year = {{2021}}, }