automatic target identification
TRANSCRIPT
AUTOMATIC TARGET IDENTIFICATION AUTOMATIC TARGET IDENTIFICATION
FOR LASER SCANNERSFOR LASER SCANNERS
Artemis Valanis, Maria TsakiriArtemis Valanis, Maria Tsakiri
National Technical University Of AthensNational Technical University Of Athens
Problem identificationProblem identification
► Demand for the highest possible accuracy in Demand for the highest possible accuracy in all kinds of applications (especially when all kinds of applications (especially when registration is necessary)registration is necessary)
► Is there a way to estimate the repeatability of Is there a way to estimate the repeatability of the measurements?the measurements?
► Could automatic target identification be Could automatic target identification be improved?improved?
► Do currently used methods have drawbacks?Do currently used methods have drawbacks?► Poor documentation of proprietary softwarePoor documentation of proprietary software
ObjectivesObjectives
►Repeatability check for measurements Repeatability check for measurements obtained with a Cyrax 2500 laser obtained with a Cyrax 2500 laser scannerscanner
►Development of new methods for Development of new methods for automatic target identificationautomatic target identification
►Comparison of old and new methodsComparison of old and new methods
Repeatability checkRepeatability check
► Data:Data: 9 scans of four targets mounted on the pillars 9 scans of four targets mounted on the pillars
of the EDM calibration baseline of the NTUAof the EDM calibration baseline of the NTUA 9 scans of five targets mounted on a wall9 scans of five targets mounted on a wall
► Processing:Processing: Mean and weighted (radiometric) position Mean and weighted (radiometric) position
using each one of the scans for each targetusing each one of the scans for each target Standard deviation of the mean and weighted Standard deviation of the mean and weighted
mean values for each one of the targetsmean values for each one of the targets Average standard deviation for all of the Average standard deviation for all of the
targets in both casestargets in both cases
Average standard deviation of mean position (mm)
EDM Baseline targets Wall targets
Mean pos.Radiometric
pos.Mean pos.
Radiometric pos.
X 0.154 0.190 0.025 0.034
Y 0.113 0.217 0.118 0.073
Z 0.228 0.267 0.058 0.090
Repeatability ResultsRepeatability Results
Automatic target Automatic target identificationidentification
►Currently used methodsCurrently used methods
maxrad: position of maximum signal maxrad: position of maximum signal strengthstrength
maxrad4: radiometric centre of the four maxrad4: radiometric centre of the four points of maximum signal strengthpoints of maximum signal strength
radcent: radiometric centre of all returnsradcent: radiometric centre of all returns
Automatic target Automatic target identificationidentification
radcentradcent maxradmaxrad maxrad4maxrad4* reflective area reflective area
of the targetof the target
Automatic target Automatic target identificationidentification
Target examinationTarget examination
►Use of fuzzy classification for Use of fuzzy classification for examination of the properties of the examination of the properties of the targetstargets
►Utilization of the fuzzy c-means Utilization of the fuzzy c-means method to classify the points of a method to classify the points of a point-cloud of a target into 3 classes point-cloud of a target into 3 classes based on their reflectivitybased on their reflectivity
Target examinationTarget examinationScan angle: 90oScan angle: 45o
Scan angle: 15o
* low reflectance
* medium reflectance
* high reflectance
New algorithmsNew algorithmsFuzzy
classification into three reflectivity
classes
Centre: average of X,Y,Z of the points of the two
classes of highest average reflectance
Fuzzypos
Fuzzyposfine
Fuzzypos
Plane fitting, system
transformation and data selection
Centre: average X,Y and Z of the points of the
lowest reflectance class points transformed back
to the original system
Gridrad &
Delrad
Creation of surface and reflectance models (5mm
spacing)
Centre: The radiometric centre calculated using
the data of the two grids
Fuzzygridrad &
Fuzzydelrad
Same as gridrad and delrad but instead of the radcent the fuzzypos algorithm is used
Algorithm Internal Accuracy EvaluationAlgorithm Internal Accuracy Evaluation
Two experiments
EDM baseline targets
Wall targets
Multiple scan collection from two
positions
Multiple scan collection from one
position
Algorithm Internal Accuracy EvaluationAlgorithm Internal Accuracy Evaluation
For each position
Reference data:
Single scan
Test data:
Single and multiple scans collected from
the same position
algorithms
Transformation between the results of the reference and
test data
Mean Absolute Error calculation for
each data series
Mean Error for each experiment
Results for the estimation of the internal Results for the estimation of the internal accuracy of the algorithms examinedaccuracy of the algorithms examined
Algorithm External Accuracy Algorithm External Accuracy EvaluationEvaluation
EDM baseline targets
Wall targets
Mean error
Reference /Test data:
•Fine scan
•Single scan
• Four merged scans
(two positions)
Reference data:
4 merged scans (90o)
Test data:
6 data series
3 & 10 m (dist)
90o, 45o & 15o
Mean absolute error
2.1
8.3
6.3
1.3 0.91.6 1.4 1.3 1.3
0.01.02.03.04.05.06.07.08.09.0
radcen
t
max
rad
max
rad4
fuzz
ypos
fuzz
yposfin
e
gridra
d
delra
d
fuzz
ygrid
rad
fuzz
ydelra
d
Me
an
Err
or
(mm
)Results for the evaluation of the external Results for the evaluation of the external accuracy of the algorithms (EDM baseline accuracy of the algorithms (EDM baseline
targets)targets)
Results for the evaluation of the Results for the evaluation of the external accuracy of the algorithms external accuracy of the algorithms
(Wall targets)(Wall targets)
3m distance3m distance
ConclusionsConclusions
► The repeatability of the measurements obtained The repeatability of the measurements obtained with a Cyrax 2500 laser scanner is very highwith a Cyrax 2500 laser scanner is very high
► Fuzzy classification is a valuable tool for obtaining Fuzzy classification is a valuable tool for obtaining a meaningful model of the data collected a meaningful model of the data collected
► The fuzzypos and fuzzyposfine algorithms:The fuzzypos and fuzzyposfine algorithms: Best performance for all combinations of scan angles Best performance for all combinations of scan angles
and distancesand distances Results of high accuracy (<1mm)Results of high accuracy (<1mm) Off-line processing possibleOff-line processing possible Algorithms availableAlgorithms available
Thank you for your Thank you for your attention!attention!