PhD Thesis - Automated Recognition of 3D CAD Model Objects in ...

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New Approach 36 Data: Scan.(PB,PP ), Δρmax Δρ ← Scan.PP .ρ − Scan.PB.ρ if |Δρ| ≤Δρmax then Scan.PP .IsRecognized ← True else Scan.PP .IsRecognized ← False end Algorithm 5: Procedure RecognizePoint matching an as-planned range point to its corresponding as-built range point. Point range measurement uncertainty: Range measurement uncertainty due to technology limitations is normally provided by laser scanner providers, though in very specific conditions (material reflectivity, with scanning directions perpendicular to the scanned surface, etc). Range measurement uncertainty generally increases with the measured range. For instance, in the case of the scanner used in this research, the provided range measurement uncertainty are: 1.5mm at 50m and 7mm at 50m for 100% reflective targets positioned perpendicularly to the scanning direction. It would therefore be appropriate to customize the threshold Δρmax with the range of each asplanned point, PP .ρ, in order to take this uncertainty into account: Δρmax = f1 (PP .ρ) Point pan and tilt angles uncertainties: Pan and tilt angle measurement uncertainties result from imperfections of the pan&tilt unit embedded in the laser scanner. They are independent from the scanned point, and are generally provided by laser scanner providers. For instance, in the case of the scanner used in this research, pan and tilt uncertainties are respectively 60μrad and 70μrad (0 ◦ 0 ′ 12 ′′ and 0 ◦ 0 ′ 14 ′′ ). These respectively translate into 0.6mm and 0.7mm precision at 10m, or6mm and 7mm precision at 100m. Theimpact of pan and tilt uncertainties on the as-planned point range measurement is that the as-planned point range may be assigned to an incorrect direction. Methods using point neighborhood analysis can be used for dealing with such uncertainties. The calculation of the threshold Δρmax could thus be further customized with the pan and tilt uncertainties, uϕ and uθ, and each as-planned range point scanning direction defined by PP .ϕ and PP .θ: Δρmax = f1 (PP . (ρ, ϕ, θ) , (uϕ,uθ)) Point reflection angle: Uncertainty in range acquisition also increases with the reflection angle between the scanning direction and the scanned surface normal vector (see illustration in Figure 3.3). If the as-planned reflection angle,
New Approach 37 α, of each as-planned range point, PP can be estimated when calculating its range, it could then be used to further customize the Δρmax threshold: Δρmax = f1 (PP . (ρ, ϕ, θ, α) , (uϕ,uθ)) Figure 3.3: Impact of the reflection angle on the range measurement uncertainty. Scanned Surface reflectivity: Range measurement uncertainty also decreases with the reflectivity of the surface it is acquired from. Thus, similarly as above, if the surface reflectivity, λ, can be obtained when calculating each as-planned range point, PP , the threshold Δρmax could then be further customized: Δρmax = f1 (PP . (ρ, ϕ, θ, α, λ) , (uϕ,uθ)) Registration error: Errors in scan registration lead to errors in model-to-scan referencing and thus may significantly affect object recognition results. Here, registration is performed using tie points (at least three non-aligned points), and it has been shown in Section 3.3 that it is possible to automatically obtain some registration error information, such as the mean registration error, ɛReg, at the end of the registration process. The mean registration error, ɛReg, could be used to adjust Δρmax, as larger Δρmax values should be used with larger ɛReg values: Δρmax = f1 (PP . (ρ, ϕ, θ, α, λ) , (uϕ,uθ) , ɛReg)
Page 1 and 2: Automated Recognition of 3D CAD Mod
Page 3 and 4: Abstract There is shift in the Arch
Page 5 and 6: Dedication This thesis is dedicated
Page 7 and 8: 3.4 Step 3 - Calculation of the As-
Page 9 and 10: D Construction of the Bounding Volu
Page 11 and 12: 5.4 Progress recognition results fo
Page 13 and 14: 5.2 Example of a model and recogniz
Page 15 and 16: Chapter 1 Introduction 1.1 Backgrou
Page 17 and 18: Introduction 3 Sub-objectives are f
Page 19 and 20: Chapter 2 Literature Review This ch
Page 21 and 22: Literature Review 7 is also often i
Page 23 and 24: Literature Review 9 Figure 2.2: Tot
Page 25 and 26: Literature Review 11 minutes (at mo
Page 27 and 28: Literature Review 13 tigated here.
Page 29 and 30: Literature Review 15 the recognitio
Page 31 and 32: Literature Review 17 recognition of
Page 33 and 34: Literature Review 19 frame of the e
Page 35 and 36: Chapter 3 New Approach 3.1 Overview
Page 37 and 38: New Approach 23 Data: Model, Scan R
Page 39 and 40: New Approach 25 frame. Therefore, f
Page 41 and 42: New Approach 27 The complexity of t
Page 43 and 44: New Approach 29 are tested for inte
Page 45 and 46: New Approach 31 effectiveness of a
Page 47 and 48: New Approach 33 (a) 3D View. (b) To
Page 49: New Approach 35 Data: PB, BVH Resul
Page 53 and 54: New Approach 39 Data: Scan,Model Re
Page 55 and 56: New Approach 41 where: α is the as
Page 57 and 58: New Approach 43 Furthermore, the ca
Page 59 and 60: Chapter 4 Experimental Analysis of
Page 61 and 62: Experimental Analysis of the Approa
Page 81 and 82: Chapter 5 Enabled Applications: Fea
Page 83 and 84: Enabled Applications: Feasibility A
Page 95 and 96: Chapter 6 Conclusions and Recommend
Page 97 and 98: Conclusions and Recommendations 83
Page 99 and 100: Appendix A Description of the STL F
Page 101 and 102:
Appendix A 87 solid name facet norm
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Appendix B 89 Figure B.1: Spherical
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Appendix B 91 Data: [x, y, z] Resul
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Appendix C 93 (a) 3D View. (b) Top
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Appendix C 95 C.1.1 Bounding Pan An
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Appendix C 97 Figure C.5: Illustrat
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Appendix C 99 Data: ϕmintemp, ϕmi
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Appendix C 101 Figure C.6: Example
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Appendix C 103 Data: {Facet} Result
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Appendix D Construction of the Boun
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Appendix D 107 Figure D.1: Illustra
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Appendix D 109 (a) MSABV not inters
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Appendix D 111 It can be noted that
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Appendix E Containment of a Ray in
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Appendix E 115 Finally, in the case
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Appendix F Calculation of the Range
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Appendix F 119 second plane perpend
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Appendix F 121 included in Algorith
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Appendix G 123 Figure G.1: 3D CAD m
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Appendix G 125 G.2.2 Step 2 - 3D mo
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Appendix G 127 G.2.4 Step 4 - Recog
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Appendix G 129 It can be seen in Fi
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Appendix G 131 Column 7: Whether th
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Appendix G 133 Table G.1 - continue
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Appendix H 143 Table H.1 - continue
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Appendix H 145 Table H.2 - continue
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Appendix H 147 [11] J. Amanatides.
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Appendix H 149 [35] G. S. Coleman a
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Appendix H 151 [57] B. Hofmann-Well
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Appendix H 153 [81] A. S. Mian, M.
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Appendix H 155 [104] L. M. Sheppard
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PhD Thesis - Automated Recognition of 3D CAD Model Objects in ...

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