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!