A flexible seam detection t echnique
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A flexible seam detection t echnique. for robotic laser welding. (Shortened English version) Jorg Entzinger. Laser bundel. Seam to Weld. Laser Focus Lens. Camera lens. Video camera. Dichroic mirror. Laser diode. Laser Focus Lens. Presentation Structure. Introduction

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A flexible seam detection t echnique

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A flexible seam detection technique

for robotic laser welding

(Shortened English version)

Jorg Entzinger


Laser bundel

Seam to Weld


Laser

Focus Lens


Camera lens

Video camera

Dichroic mirror

Laser diode


Laser

Focus Lens


Presentation Structure

  • Introduction

  • Lens & camera calibration

  • Image undistortion

  • Seam Detection


Detect seams

Track & learn seams

Laser weld seams

Process control

Quality control

Functions of the Multifunctional Welding head


Specialities of this welding head

  • Multifunctionality

    All needed technology is integrated in one machine

  • Compactness

    Flexible in use for complex geometries

  • Lightweight

    For higher accuracies with the use of robots


Assignment

  • Develop a system that can compensate for lens distortions

  • Develop a system to determine the exact position of the workpiece with respect to the welding head from camera images


Distortion Types

  • Perspective distortions

  • Camera distortions (skew, non-squareness of pixels)

  • Lens distortions (radial: barrel/pincushion)

  • Noise (dust, bad focussing, CCD measurement noise)

Normal Perspective Skew Barrel Pincushion


C++

Read parameters

from file

Generate look-op table

of pixel displacements

Aquire camera image

Undistort image

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Parameter estimation

Write Params to File

Program Structure


Program Structure

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Parameter estimation

Write Params to File

C++

Read parameters

from file

Generate look-op table

of pixel displacements

Aquire camera image

Undistort image


The calibration-pattern

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Estimate parameters

Write params to file


Pictures of theCalibration-pattern

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Estimate parameters

Write params to file


Identified Keypoints

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Estimate parameters

Write params to file


Sorted Keypoints

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Estimate parameters

Write params to file


Perspective

Camera

Parameter estimation (1)


Parameter estimation (2)

Barrel Pincushion

or


Estimation refinement

Homography was calculated without considering radial distortions

 Distortions are calculated from an inaccurate homography

 The estimations must be refined, all parameters are optimized at the same time


Program Structure

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Parameter estimation

Write Params to File

C++

Read parameters

from file

Generate look-op table

of pixel displacements

Aquire camera image

Undistort image


Program Structure

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Parameter estimation

Write Params to File

C++

Read parameters

from file

Generate look-op table

of pixel displacements

Aquire camera image

Undistort image


Undistortion


An Original (Distorted) Picture


Test Result


Test Result

Original Undistorted


Simulated Distortion Test

Original Undistorted


Result

Original Undistorted


Amout of Distortion

Pixel movement in %

Position on image diagonal


Programma Structuur

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Parameter estimation

Write Params to File

C++

Read parameters

from file

Generate look-op table

of pixel displacements

Aquire camera image

Undistort image


Program Structure

MATLAB

Make calibration pattern

Take pictures

Identify keypoints

Sort keypoints

Parameter estimation

Write Params to File

C++

Read parameters

from file

Generate look-op table

of pixel displacements

Aquire camera image

Undistort image

Determine seam location

Move Robot


Changes in camera image due to a change of relative position


Changes in camera image due to a change of relative position


World Coördinates

  • How many millimeters in reality

    is 10 pixels in the image?

  • If the image moves to the right, in what direction did the robot move?

  • Where is the camera

    with respect to the welding spot?


Test objects


Following a seam


Thank you

for your attention

Jorg Entzinger


Rotatie Matrix


Rodrigues Parameters


Rotatie Matrix  Rodrigues Parameters


Schatten van de Parameters (1)

met


Aanbevelingen

  • Pixel-millimeter schaling hoogte afhankelijk maken

  • Bepaling naad-positie minder afhankelijk maken van handmatige instellingen

  • Zorgen voor goede afhandeling als de naad dicht bij de kruising van de lijnen komt

  • Goede gebruikers-interface voor camera & lens calibration maken


Camera & Lens distortions

  • Perspective distortion

  • Skew distortion

  • Radial distortions (barrel & pincushion)

  • Noise

Normal Perspective Skew Barrel Pincushion


Why Camera Calibration?


Detectie van Lijnen


Scheiden van Lijnen in het Kruis


Detectie van de Lasnaad


Parameters schatten

Er worden subsets gemaakt van

Datapunten uit 4 plaatjes, bijvoorbeeld:

Subset 1 Subset 2 Subset 3 ...

Dataset 1 Dataset 2 Dataset 3

Dataset 2 Dataset 4 Dataset 5

Dataset 3 Dataset 6 Dataset 7

Dataset 4 Dataset 8 Dataset 8

Voor elke subset wordt een calibration

uitgevoerd

MATLAB

calibration patroon

maken

Foto’s nemen

Keypoints identificeren

Keypoints sorteren

Parameters schatten

Parameters naar

bestand schrijven


Parameter Schattingen Op Basis Van Meerdere Subsets

RMS alfa f


Schatten van de Parameters (2)

Homografie (per plaatje):8 DOFs

Plaatje afhankelijk: 6 DOFs

(3 rotatie en 3 translatie)-

Over voor schatting camera

parameters:2 DOFs

Er zijn 5 camera afhankelijke parameters,

dus er is minstens 2½ plaatje nodig


Verstorings-gebied fotocamera


Experiment


Experiment


Meetfouten

Y-fout [mm]

X-positie [mm]

Z-fout [mm]

X-positie [mm]

Totaal-fout [mm]

X-positie [mm]


A flexible seam detection technique

for robotic laser welding

Jorg Entzinger


Dagplanning

13:00 – 13:45Presentatie

13:45 – 14:00Vragen uit de zaal

Jorg & Examencommissie

14:00 – 14:30Demonstratie in het lab

14:30 – 15:30Ondervraging

Rest

14:00 – 15:30Rondleiding door Niels en/of

Koffie/Thee in WB (Horst) kantine

Iedereen

15:30 – 16:00Diploma-uitreiking & felicitatie (WB-Z109)

16:15 – 18:00Borrel & Demonstraties in het Lab

(WB-Hal IV = Westhorst)


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