Page - 32 - in Joint Austrian Computer Vision and Robotics Workshop 2020
Image of the Page - 32 -
Text of the Page - 32 -
4.1.Weighting
Firstan intensityestimation αˆm foracertainmea-
surementm is calculatedbasedon thesingleparticle
p∈P tobeweightedandthe influenceofallalready
definedsources sˆ∈Ψassumingthemodelpresented
inEquation 1:
αˆ(m) =αbgr+ αp
4 ·pi ·dp(m)2 + ∑
sˆ∈Ψ αs
4 ·pi ·ds(m)2
(2)
UsingEquation2 the relativemeansquareerrorcon-
sidering allmeasurements is calculated like:
ermse= 1
|Γ| ∑
m∈Γ ( αˆm−αm
αˆm )2
(3)
where |Γ| is the number of measurements. The
weight for a singleparticle is thencalculated like:
wp= 1
1+ermse (4)
After all particle weights have been updated the
weights are normalizedsuch that ∑
p∈Pwp= 1.
4.2.Re-SamplingandClustering
During re-sampling a certain percentage of parti-
cles with the highest weights stay the same, while
another percentage of particles with the smallest
weightsareomittedandnewlydrawnfromauniform
distribution over the search space. The remaining
particles are re-sampled by adding Gaussian Noise
to the intensityαp and position based on the parti-
clesweight:
[x′p,y ′
p,α ′
p]
T∼N (
[xp,yp,αp]
T, diag(σpos,σpos,σint)
1+wp )
(5)
The total number of particles stays the same. After a
defined number of iterationsk the particles are clus-
tered using the mean shift algorithm as suggested by
[3]. The cluster centroids have the same structure as
asingleparticleandarethenevaluatedbytheweight-
ing algorithm described in section 4.1. If the weight
of a cluster surpasses a defined thresholdϕ the cen-
troid is believed to be a real source and added to the
growing setofpredictedsourcesΨ.
5.ExperimentalEvaluation -ENRICH2019
Aspartof theTUGrazRoboticsTeamTEDUSAR
weparticipated in theEuropeanRoboticsHackathon TotalParticles 2000
RandomNewParticles 10%
SustainParticles 10%
MaxIterationsT 1000
ConfidenceThresholdϕ 0.9
Clustering Intervalk 20 iter
PositionDeviationσpos 0.5
IntensityDeviationσint 0.4
Table 1: Hyper-parameters used for the ENRICH
2019
Figure 1: Source estimation based on live measure-
mentdataduring theENRICH2019inZwentendorf.
- ENRICH 2019 at the nuclear power plant Zwen-
tendorf, Austria1 and were able to test our particle
filter approach under real world conditions. An au-
tonomous robot created a 3D map of the interior
while our approach created the mathematical model
of the real radiation sources and the radiation con-
tamination. The parameters used are shown in Table
1.
An experimental result can be observed in Figure
1. In this experiment two sources were placed in a
largerroom. After traversingtheroomandcollecting
radiation measurements our approach correctly pre-
dicted the locationand intensityof the twosources.
6.ConclusionandFutureWork
In thispaperwepresented theadaptationofanap-
proachbasedonaparticlefiltertodeterminetheloca-
tionandintensity foranarbitraryandunknownnum-
ber of stationary radiation sources. This approach
has been successfully tested and proven to be appli-
cable in real world scenarios, like an accident in a
nuclear facility. Future work will focus on reducing
thenumberofhyper-parameters.
1www.enrich.european-robotics.eu
32
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