Page - 31 - in Joint Austrian Computer Vision and Robotics Workshop 2020
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UGVRadiationMappingusingaParticleFilter
AlexanderPermann,DanielHettegger,GeraldSteinbauer
InstituteofSoftwareTechnology,TUGraz
alexander.permann@student.tugraz.at, daniel.hettegger@alumni.tugraz.at
steinbauer@ist.tugraz.at
Abstract. We present and evaluate a particle fil-
ter based approach to predict the location and emis-
sionintensityofanarbitraryandunknownnumberof
stationary nuclear radiation sources from measure-
ment data taken by an autonomously navigating un-
manned ground vehicle (UGV).
1. Introduction
Due to the threat for humans caused by radiation
and the associated difficulties after a nuclear disas-
ter it is crucial to establish save methods of estimat-
ing the radiation distribution in certain affected ar-
eas. For this purpose we suggest to record radia-
tionmeasurementusinganautonomousUGV.These
measurements are then processed by an adapted par-
ticle filter to generate a detailed radiation distribu-
tion model of the affected area. The approach pre-
sented in this paper has been successfully tested in
realisticconditionsat theENRICH2019—European
Robotics Hackathon, where live radiation sources
had to be detected inside the nuclear power plant in
Zwentendorf,Austria.
2.RelatedResearch
In [1] Eric T. Brewer used an autonomously fly-
ing aerial platform to detect and locate a single ra-
dioactive point source using a particle filter. In [2]
M. Morelande et al. compare the performances of a
maximum likelihood estimator and a Bayesian esti-
mator approach to deal with an unknown number of
sources. D. Shah et al. present a particle filter in [3]
thatmanages to locatemultiple radiationsources.
3.ProblemDescription
The setting is represented by a set Θ of unknown
radiation sourcessand a setΓof radiation measure-
mentsm. Thegoal is togenerateasetΨofestimated sources sˆ, that fits the number and intensities of the
real sources accurately. Each set holds elements de-
finedbyacertainlocationxiandyiandanequivalent
radiationdose rateαi inSvs−1 that either represents
the actual measurement for the set Γ or the theoret-
ical dose rate that would be measured at the exact
position of a source for the sets Θ and Ψ. In general
formodellingtheradiationintensityatacertain loca-
tion lbasedonasetofsourcesΘ,weassumethat the
radiation follows the principle of superposition and
the inverse-square-law which has been shown to be
applicablebymultiple formerapproaches. [1,2]:
α(l) =αbgr+ ∑
s∈Θ αs
4 ·pi ·ds(l)2 (1)
whereαbgr denotes the known background radiation
andds(l) theeuclideandistancebetweenthelocation
l and the sources.
4.ParticleFilter
In contrast to common particle filter use cases in
robotics (e.g., estimatinga robotsposition), it isnow
necessary todetectmultiplesources thatcanco-exist
at thesametimeatdifferentpositions. In thiscontext
particlesarepredictionsofpotential sources [3]with
each particlep∈P being represented similar to real
sourcesbyp=<xp,yp,αp,wp>withanadditional
weightwp that is related to theprobability that acer-
tain particle has the parameters of a real source. At
first theparticlesare initializeduniformlydistributed
on theplanewhere themeasurements tookplaceand
givenarandomintensitywithinthesamerangeofthe
measurement results. The algorithm then iteratively
performs the twostepsofweightingand re-sampling
and adds estimated sources sˆ to the growing set Ψ
until a maximum number of iterationsT is reached
andΨ representsaconsistent estimation forΘbased
on themeasurementsΓ.
31
Joint Austrian Computer Vision and Robotics Workshop 2020
- Title
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Editor
- Graz University of Technology
- Location
- Graz
- Date
- 2020
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Categories
- Informatik
- Technik