1. Validation of the Measurement Performance of a 3-D Vision Sensor by means of a Coordinate Measuring Machine
Giovanna Sansoni1, Simone Carmignato2, Enrico Savio2
1Laboratory of Optoelectronics, DEA - Dept. of Electronics for the Automation University of Brescia, Italy
2Laboratory of Industrial and Geometrical Metrology, DIMEG – Dept. of Mechanical Engineering and Management
University of Padova, Italy

2. Context of the work Optical-Contact CAD The optical sensor: OPL-3D
Development of a novel methodology for the reverse engineering of complex, freeform surfaces, combining three-dimensional vision systems and Coordinate Measuring Machines
The optical sensor: OPL-3D
View alignment
Volumetric fusion BS MI
Single point clouds, Multiple point clouds, 3D desctiptive models
Optical-Contact CAD PD
CAM RP
Re-styling

3. The Optical-Contact CAD
Point clouds
Descriptive models
Data elaboration
‘rough’ CAD model
Contact digitization
‘accurate’ CAD model
CAM RP
Re-styling

4. The optical sensor: OPL-3D
Optical active triangulation
Time-space coding of the meas. volume
Fringe projector
Video camera
Assembly system

5. Point clouds

6. Multi-view integration of point clouds

7. Embedded ‘Check’ procedure (1)
Performed after the calibration
Based on the use of control markers
Calibration master

8. Embedded ‘Check’ procedure (2)
Master dependent
Calibration masters for computer-vision applications often priviledge low-cost aspect rather than the accuracy
Designed for single view acquisition
Unable to grasp any information about the influence of the alignment for multi-view acquisition
What about the accuracy of OPL-3D?

9. …Monitoring the accuracy
Hardware:
principle of measurement,
mechanical stability..
Environment:
illumination,
temperature
Vibrations…
Object:
Surface finishing,
Colour and texture,
accessibility
No standard available!
Measurement Uncertainty
Extrinsic factors:
Operator skill,
Surface cleanliness
Measurement time
Clamping system
Measuring strategy:
measuring field,
calibration procedure
coordinate system
Data processing:
Filtering,
Registration of multiple views
Algorithm sophistication
Nella misura con i sistemi ottici l’accuratezza è influenzata da molti fattori. Oltre ai fattori hardware intrinseci del sistema, legati alla stabilità meccanica, agli errori geometrici della struttura ed alle caratteristiche del sensore e dell’elettronica, hanno una forte influenza anche variabili ambientali quali le condizioni d’illuminazione e la temperatura. Anche le caratteristiche dell’oggetto (rugosità superficiale, colore, opacità, accessibilità, curvatura delle superfici e presenza di spigoli), le strategie di misura (campo di risoluzione impiegato, metodologia di calibrazione e sistema di coordinate scelto per l’allineamento) e gli algoritmi di elaborazione (filtraggio dei punti acquisiti, registrazione di più viste, eccetera) incidono molto sull’incertezza. Infine non è da trascurare il peso di altri fattori estrinseci come l’azione dell’operatore, la pulizia delle superfici ed il sistema di fissaggio.

10. Evaluation of the accuracy of OPL-3D: substitution approach
Calibrated Object
CMM used as the link of the traceability chain
Full optical digitization by means of OPL-3D
Accurate CAD Model
Point clouds
CAD-Point cloud Deviations
CMM Uncertainty
Well known substitution approach, in which repeated measurements are carried out on a calibrated object
Accuracy evaluation of OPL-3D

11. The calibrated object Stainless steel, long term stability
Volume: 230mm x 100mm x 20 mm
Free form surface
Representative of a typical measurement application
Spheres for accurate alignment
White painted
Substitution approach valid only for measurement tasks similar to that one represented by the calibrated object
Reasonable estimate of the accuracy for a wider range of applications

12. Accuracy of the CAD model
The accurate CAD Model
Touch probe CMM
MPE = L/300 µm (L: mm)
Accuracy of the CAD model
CAD model from the CMM
In this step, the CMM was used as the link to the traceability chain, to obtain an accurate CAD model of the measured physical object. To measure the turbine blade, a CMM with Maximum Permissible Error (MPE of L/300 µm (L in mm) was used. The CMM was equipped with dedicated software to measure freeform surfaces. The probing configuration was used in scanning contact mode, gathering a reasonably high density of points with low measurement uncertainty. During digitization, the CMM probing direction matches the normal direction to the CAD surfaces; therefore, measurement errors due to probe tip radius compensation are kept to a minimum and can be neglected. Other effects, such as roughness of the object, temperature, humidity, etc. were under control. The result of this step is a “calibrated” CAD model, built using points measured on the CMM.
To assess the accuracy of this model, the object was measured again on the CMM using the CAD model previously obtained as the reference. The comparison was carried out by computing the local deviations between the reference CAD model and a proper number of points measured by the CMM, in different locations with respect to the points gauged to obtain the reference CAD. The comparison was performed using the commercial software HOLOS-UX (Holometric Technologies GmbH). The results shown that the maximum deviation was included in 0.01mm for 95% of points.

13. The optical acquisition
Single view acquisition: 240mm x 180mm
Multiview acquisition (3 v) 120mm x 89mm
From the Check mm
From the Check 70mm
The optical acquisition of the turbine blade was accomplished by using two different optical configurations of OPL-3D. In the former, the target was acquired using a field of view approximately equal to 240mm x 180mm: in this way, a single point cloud was obtained in correspondence of the whole surface. In the latter, the field of view was reduced to 120mm x 80mm. In this configuration, the whole surface could be achieved by three points clouds, partially overlapped. A proper number of small markers were placed on the surface, to be used as anchoring points by the alignment software, to reduce as more as possible the accumulation of the alignment errors. The Type A uncertainties evaluated by the ‘Check’ procedure embedded in OPL-3D were of 100mm and 70mm respectively (for each view).

14. CAD-Point cloud Deviations
Performed by Imageware Surfacer (SDRC Corporation)
Evaluation of local deviations between the accurate CAD model from the CMM and the point clouds from OPL-3D
Criticality of the alignment of the point clouds to the CAD model
Best fit using all measured points
Best fit using the sphere points
Best fit using the points on the freeform surfaces
The 3D data optically acquired were compared to the accurate “reference” CAD model. This work was carried out by using the market available Imageware Surfacer software (SDRC Corporation), and consisted in the evaluation of the local deviations existing between the CAD model and the 3D data-sets. The critical aspect of this task was the alignment of the 3D point clouds to the CAD reference. Different strategies were tested, all based on the least-squares algorithm. These are (i) the best-fit using all measured points, (ii) the best-fit using only the points measured on the spheres, and (iii) the best-fit using only the points on the freeform surface.
Strategies 1 and 3 delivered similar results, whereas Strategy 2 was overestimating. This is reasonable, since the spheres are external to the turbine blade and often close to the border of the CCD during digitization.
In addition, the high curvature of the sphere surface resulted into a higher distortion of the 3D shape measured by OPL-3D. Thus, the results from Strategy 1 were chosen to represent the system performance.

15. CAD-Point cloud Deviations: single view

16. CAD-Point cloud Deviations: multiple views

17. Bottlenecks Calibration master Alignment errors
Need of skilled alignment strategies
Need of view- fusion
Need of accurate polygonal models
Scanning Number
Planarity (mm) 1
0,2485 2
0,2470 3
0,2460
average
0,2472
The results from the statistical analysis performed over the deviations of the single point cloud with respect to the “reference” CAD are in total accordance with the Type A uncertainties evaluated by the optical system.
The results from the statistical analysis performed over the deviations of the overlapped point clouds with respect to the “reference” CAD show that the measurement performance strongly decreases in this case.
It is by no means surprisingly when the following aspects are considered.
Firstly, the measurement error in correspondence with each partial view is higher with respect to that one expected from the ‘Check’ procedure embedded in OPL-3D. In turn, this is due to the inaccuracy of the calibration master, which obviously cannot be accounted for by that procedure.
Secondly, the views were simply aligned, instead of being fused into a single one. This aspect is extremely critical, due to the fact that the fusion of the point clouds inherently reduces the variability of the measured data, because it is accomplished by eliminating the worse data points, as well as any data redundancy. A number of specialized market available software modules perform this task. In our case, however, the alignment was carried out by a self-made alignment module, which showed up to be not as accurate as it was expected (actually, these tests turned out to be very useful also to verify the performance of this software module).
Thirdly, the regularity of shape of the turbine blade was very critical for the alignment. It is quite noting that this criticality equally holds whenever the shapes to be jointed do not present 3D details that may be used as anchoring points between adjacent views, even when physical markers are used.