PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

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8.2. Map building experiments 137 (a) (b) Figure 8.6: (a) Piece-wise planar world map of the ”Office” environment consisting of 5 linked sub-maps (3D view). (b) Enlarged detail.
8.2. Map building experiments 138 Figure 8.7: Laser map (blue) <strong>and</strong> bird’s eye view of the visual map (green) overlayed.
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Graz University of Technology Insti
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Abstract Visual map building and lo
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Contents 1 Introduction to mobile r
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CONTENTS vi 7.1.5 Sub-map linking .
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1.1. Localization and map building
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1.3. What has already been achieved
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1.5. How can it get solved? 6 fully
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1.6. Contribution of this thesis 8
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1.7. Structure of the thesis 10 com
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2.2. Localization from point featur
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2.2. Localization from point featur
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2.4. Localization from plane featur
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2.5. Summary 18 or not. Clearly thi
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Chapter 3 Local detectors Research
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3.1. Interest point detectors 22 fu
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3.2. Scale invariant detectors 24 r
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3.2. Scale invariant detectors 26 (
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3.2. Scale invariant detectors 28 3
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3.3. Affine invariant detectors 30
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3.4. Comparison of the described me
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3.4. Comparison of the described me
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44 But using a plane to plane homog
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4.2. Representation of the detectio
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4.3. Detection correspondence 48 th
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4.4. Point transfer using the trifo
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4.5. Ground truth generation 52 usi
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4.6. Experimental evaluation 54 Fig
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4.6. Experimental evaluation 56 rep
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4.6. Experimental evaluation 58 MSE
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4.6. Experimental evaluation 60 mat
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4.6. Experimental evaluation 62 vie
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4.6. Experimental evaluation 64 vie
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4.6. Experimental evaluation 66 rel
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Chapter 5 Maximally Stable Corner C
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5.1. The MSCC detector 70 (a) (b) (
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5.2. Region representation 72 400 (
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5.3. Computational complexity 74 6.
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5.5. Detection examples 76 paramete
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5.5. Detection examples 78 Figure 5
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5.6. Detector evaluation: Repeatabi
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5.7. Combining MSCC with other loca
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5.7. Combining MSCC with other loca
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Page 99 and 100: 6.1. Wide-baseline region matching
Page 101 and 102: 6.1. Wide-baseline region matching
Page 103 and 104: 6.2. Piece-wise planar scene recons
Page 117 and 118: Chapter 7 Living in a piecewise pla
Page 119 and 120: 7.1. Map building 112 s,R,t sub-map
Page 121 and 122: 7.1. Map building 114 x = (x 1 ...x
Page 123 and 124: 7.1. Map building 116 distance is u
Page 125 and 126: 7.1. Map building 118 normalization
Page 127 and 128: 7.2. Localization 120 where N = |D
Page 129 and 130: 7.2. Localization 122 registration
Page 131 and 132: 7.2. Localization 124 (a) (b) (c) F
Page 133 and 134: 7.2. Localization 126 3D structure
Page 135 and 136: 7.2. Localization 128 other landmar
Page 137 and 138: Chapter 8 Map building and localiza
Page 139 and 140: 8.2. Map building experiments 132 8
Page 141 and 142: 8.2. Map building experiments 134 D
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Page 147 and 148: 8.2. Map building experiments 140 F
Page 149 and 150: 8.3. Localization experiments 142 8
Page 151 and 152: 8.3. Localization experiments 144 F
Page 153 and 154: 8.3. Localization experiments 146 F
Page 155 and 156: 8.3. Localization experiments 148 8
Page 157 and 158: 8.3. Localization experiments 150 (
Page 159 and 160: Chapter 9 Conclusion More than 25 y
Page 161 and 162: 9.1. Future work 154 Map building a
Page 163 and 164: 9.1. Future work 156 information ca
Page 165 and 166: A.1. Projective ellipse transfer 15
Page 167 and 168: A.1. Projective ellipse transfer 16
Page 169 and 170: A.2. Affine approximation of ellips
Page 171 and 172: Appendix B The trifocal tensor and
Page 173 and 174: Bibliography [1] S. Atiya and G. Ha
Page 175 and 176: 168 [31] F. Fraundorfer and H. Bisc
Page 177 and 178: 170 [61] U. Köthe. Edge and juncti
Page 179 and 180: 172 [92] F. Schaffalitzky and A. Zi
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PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

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