DEVELOPMENT AND EVALUATION OF A MEASUREMENT AND CALCULATION MODULE FOR OFFSETS WITH RESPECT TO A STRECHED WIRE BASED ON PHOTOGRAMMETRY AT CERN

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1 DEVELOPMENT AND EVALUATION OF A MEASUREMENT AND CALCULATION MODULE FOR OFFSETS WITH RESPECT TO A STRECHED WIRE BASED ON PHOTOGRAMMETRY AT CERN Servet Lapardhaja NTUA Undergraduate Student 28/09/2018

2 Summary 1. Introduction 2. Development of algorithm Automatic target detection position determination Decoding of encoded and finding of homologous points for non-coded 3. Usage of the LGC2 for the adjustment 4. Offsets calculation with respect to stretched wire based on photogrammetry 5. Conclusions 6. Outlook 28/09/2018 Servet Lapardhaja 2

3 1. Introduction (1) Offset s Executed by the Survey team Important for the accelerator alignment Measured by ecartometry with precision of ± 15 μm tο ± 20 μm for single (repeatability) Ecartometry Ecartometers installed on fiducials and the perpendicular distance to the stretched wire is measured Precision of ±50 μm for 150 m wire (adjustment) Manual work, time consuming, up to 500 m/day Prone to errors due to human factor and need to limit human presence due to future increased levels of radioactivity 28/09/2018 Servet Lapardhaja 3

4 1. Introduction (2) About the work Aim is the offsets calculation with respect to a stretched wire The offset s are important for the alignment task The alignment is critical for the increase of the performance of the beam Automation of the Replacement of the manual way of Development of algorithm for the determination of the Development of algorithm for the wire determination (Already available) Calculation of the offset distances 28/09/2018 Servet Lapardhaja 4

5 1. Introduction (3) Present photogrammetric equipment Nikon Camera D3X 24.5MP Encoded & Non-Coded Scale bars Black fishing wire of 0.3 mm diameter Advantages of photogrammetry for offset s Automatic procedure, independent from user (future installation of cameras on frame) Non-contact, wire and are measured simultaneously Equivalent precision with ecartometry Abatement of human error 28/09/2018 Servet Lapardhaja 5

6 2. Development of algorithm Automatic target detection Before the execution of the algorithm the user should provide the following parameters: Number of bits of the used target (12, 14 or 20) Color of the target (black or white) Whether the are retroreflective or conventional Minimum diameter of the in pixel Maximum diameter of the in pixel The maximum ratio of half major to half minor axis ( a b ) The contrast value for the detection of edges The pretreatment method Input parameters Image pretreatment & binarization detection Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 6

7 2. Development of algorithm Automatic target detection 3 methods of pretreatment Adaptive histogram equalization with Guided filter [1] Adaptive histogram equalization with Gaussian filter [2] Sharpening of the image (recommended) [3] Binarization of the image Otsu s method which is applied on pretreatments [1] and [2] The method requires histogram to have bimodal distribution which is not always the case Application of a simple threshold which is applied after the image sharpening, where the contrast is already enhanced threshold = (maxvalue + minvalue) 40% Input parameters Image pretreatment & binarization detection Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 7

8 2. Development of algorithm Automatic target detection Original image Input parameters Image pretreatment & binarization detection After sharpening Binarization Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 8

9 2. Development of algorithm Automatic target detection Usage of the MATLAB regionprops function with conditions, which returns a set of properties for connected components (weighted centroid, area, major and minor axis, orientation). A regionprops = 1 ± range A calculated Half major axis Half minor axis Type of target Retro reflective Conventional < input value Minimum target size (Diameter in pixels) Area (pixels 2 ) Range >=3-5 >= >=5-7 >= >=7-10 >= >=10 >= >=3-5 >= >=5-7 >= >=7-10 >= >=10 >= Input parameters Image pretreatment & binarization detection Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 9

10 % 2. Development of algorithm Automatic target detection Input parameters Image pretreatment & binarization Percentage of detected number of by developed algorithm with respect to the number of detected by AICON Project Average percentage out of the 7 projects is 101%, thus almost the same amount (~16000) were detected from both AICON software and the developed algorithm detection Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 10

11 2. Development of algorithm position determination Local regionprops on cropped image Local regionprops on resized by a factor cropped image Ellipse fitting (most reliable and precise) Input parameters Image pretreatment & binarization detection Steps to do the position determination by ellipse fitting 1. Crop each target from the whole image 2. Sub-pixel edge detector elaborated by Agustín Trujillo Pino et al. 3. Ellipse adjustment on the detected edge points using parametric equation of ellipse with least squares Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 11

12 2. Development of algorithm position determination Precision achieved with ellipse fitting after the bundle adjustment, in 2D is at a level of ± 0.03 pixel to ± 0.05 pixel which is nearly equivalent to AICON Input parameters Image pretreatment & binarization detection Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 12

13 2. Development of algorithm position determination 2D coordinates residuals are more concentrated to lower residual values with AICON and ellipse fitting rather than with the rest of algorithms Input parameters Image pretreatment & binarization detection Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 13

14 2. Development of algorithm position determination Edges detected Ellipse fitting Input parameters Image pretreatment & binarization detection Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 14

15 2. Development of algorithm Decoding of encoded Steps to do the decoding 1. Crop each target from the whole image 2. Binarize each image using threshold = 0.55 PixelValue, where the pixel value is the intensity value of the center of the target 3. Spread points on the code segments (12, 14 or 20 points depending on the number of bits) 5 times with different rotations 4. Find the minimum value generated from binary sequence and check if it is generated more than once from the different rotations 5. Find correspondence of the number found with the AICON codes Input parameters Image pretreatment & binarization detection Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 15

16 2. Development of algorithm Decoding of encoded BIT EXTRA ROTATIONS θ -5+θ 0+θ 5+θ 10+θ 14-8+θ -4+θ 0+θ 4+θ 8+θ 20-6+θ -3+θ 0+θ 3+θ 6+θ Input parameters Image pretreatment & binarization detection The red and the green points have extra rotations of 4 and 8 degrees respectively. While the cyan and magenta points have extra rotations equal to -4 and -8 degrees. Process of finding the minimum value for the code AICON ID 170 Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 16

17 2. Development of algorithm Bundle adjustment & Calibration Calculation of the interior orientation parameters (xo, yo, Ck, A1, A2, A3, B1, B2, C1, C2) Calculation of the exterior orientation parameters (X, Y, Z, ω, φ, κ) Calculation of 2D coordinates by ellipse fitting input s in AICON Bundle adjustment & Calibration 3D coordinates calculation Precisions estimation (RMS) Precisions achieved ± 3 μm to ± 8 μm, depending on configuration Project RMS X (μm) RMS Y (μm) RMS Z (μm) Input parameters Image pretreatment & binarization detection Decoding of encoded Bundle adjustment & calibration /09/2018 Servet Lapardhaja 17 Finding of homologous

18 2. Development of algorithm Finding of homologous non-coded Decoding can not be applied to find homologous points Application of collinearity equations with least squares Intersections among all combinations of uncoded are tried Homologous points are found, if the intersection precision is higher than 50 μm on each component Input parameters Image pretreatment & binarization detection Decoding of encoded Bundle adjustment & calibration Finding of homologous 28/09/2018 Servet Lapardhaja 18

19 3. Usage of the LGC2 LGC2 input file creation & adjustment Integration of the 2D wire and target s Correct image coordinates from distortion Format of s into observations for LGC2 (UVEC unity vector) Extraction of parameters of the exterior orientation from AICON camera calibration and format them Creation of FRAMEs for each camera (photo) and the wire Computation of final coordinates for points on the wire and the Calculation of precision LGC2 input file creation & adjustment Offset 28/09/2018 Servet Lapardhaja 19

20 3. Usage of the LGC2 LGC2 input file creation & adjustment Unity vectors LGC2 input file creation & adjustment Offset r = x 2 + y 2 + z 2, i = x r, j = y r, k = z r Approximate object coordinates s: Bundle adjustment report Wire points: Intersection of planes (creation of line-wire) and rays from projection center precision ±7.0 μm /m Wire precision ±6.5 μm /m (for the LHC projects ~1m distance) 28/09/2018 Servet Lapardhaja 20

21 4. Offsets calculation Ten project tests were done at the LHC, where photos were captured for each project. LGC2 input file creation & adjustment Offset Camera specifications Nikon D3X 28MM AICON Metric Lens Flash 1/8 with diffusion plate on and flash of large angle ISO 320 Aperture 11 Exposure time 1/125 28/09/2018 Servet Lapardhaja 21

22 4. Offsets calculation Offsets can be distinguished to radial and vertical LGC2 input file creation & adjustment Offset 3D offset distances were calculated due to the lack of inclinometers for the link to the gravity with precision The idea is to create a panel which will have a certain number of cameras and will be carried on the survey train in order to do the offset s. Four configurations were tested. A B C D 28/09/2018 Servet Lapardhaja 22

23 σd (mm) 4. Offsets calculation Precision of the 3D offset distance for the various configurations 4 cameras configuration provides significant gain in precision compared to 3 cameras and equivalent precision with 5 and 6 cameras Precision of the 3D offset distance with respect to a stretched wire at a level of ±15 μm to ±20 μm for the fiducials Fiducial on the bar Fiducial Front point on the bar Back point on the bar LGC2 input file creation & adjustment Offset 3 photos 4 photos 5 photos 6 photos All photos 28/09/2018 Servet Lapardhaja 23

24 5. Conclusions Offset s by photogrammetry are promising due to: Automation of the and absence of the human factor Simultaneous of the stretched wires and fiducials Reliable target detection and decoding Precision of the image s for the in 2D at a level of ± 0.03 pixel up to ± 0.05 pixel Typical residuals in the object space for the and the wire are smaller than 10 µm at 1.0 m distance Precision of 3D offset distance ±15 μm to ±20 μm 28/09/2018 Servet Lapardhaja 24

25 6. Outlook Future developments: Creation of a panel which will include 4 cameras with a base of m and mounted on the survey train Installation of 2 precise bi-directional inclinometers for the link to the gravity Upgrade of the Collimator survey train Distinguish after the link to the gravity, the 3D offset distance to radial and vertical offsets Development of a blunder detection at the LGC2 Development of a more sophisticated way of intersections to find homologous points for large number of non-coded 28/09/2018 Servet Lapardhaja 25

26 Thank you for your attention

27 Back-up slides

28 Range Area (pixels 2 )

29 Inner + relative orientation, AICON bundle adjustment Detection and of wire in 2D Detection and of points in 2D Intersection of multiple planes and lines in LGC2 Approx. Wire position, conversion to LGC2 format Decoding of coded, homologous points

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31 Sub-pixel edge detection (Trujillo-Pino et al., 2013): Usage of intensity values I 0 and I 1 at the edge Estimation of edge by a second order curve y = a + bx + cx 2 Windows of 5 x 3 pixels Output data: x, y coordinates of the edge Normal vector N The intensities I o and I 1 on both sides The curvature K