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

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167 [14] J. Buhmann, W. Burgard, A. Cremers, D. Fox, T. Hofmann, F. Schneider, J. Strikos, and S. Thrun. The mobile robot Rhino. AI Magazine, 16(1), 1995. [15] J. Canny. Finding edges and lines in images. In MIT AI-TR, 1983. [16] G. Carneiro and A. Jepson. Phase-based local features. In Proc. 7th European Conference on Computer Vision, Copenhagen, Denmark, pages I: 282–296, 2002. [17] H. Christensen, N. Kirkeby, S. Kristensen, and L. Knudsen. Model-driven vision for in-door navigation. Robotics and Autonomous Systems, 12:199–207, 1994. [18] D. Comaniciu and P. Meer. Mean shift analysis and applications. In Proc. 7th International Conference on Computer Vision, Kerkyra, Greece, pages 1197–1203, 1999. [19] T. Cormen, C. Leiserson, and R. Rivest. Introduction to Algorithms. MIT Press, Cambridge MA, 1990. [20] I. Cox. Blanche: An experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation, 7(2):193–204, 1991. [21] A. Davison and D. Murray. Simultaneous localization and map-building using active vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):865–880, 2002. [22] A. J. Davison. Mobile Robot Navigation Using Active Vision. PhD thesis, University of Oxford, 1999. [23] G. de Souza and A. Kak. Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2):237–267, 2002. [24] D. DeMenthon and L. Davis. Model-based object pose in 25 lines of code. International Journal of Computer Vision, 15(1-2):123–141, 1995. [25] R. Duda, P. Hart, and D. Stork. Pattern Classification. Wiley, 2001. [26] P. J. Elbischger. Circular data - About the representation and calculation of oriented and directed 2D data. Technical Report ICG-TR-1, Institue for Computer Graphic and Vision, Graz University of Technology, 2003. [27] S. P. Engelson and D. V. McDermott. Error correction in mobile robot map learning. In Proc. IEEE International Conference on Robotics and Automation, Washington D.C., US, pages 2555–2560, 1992. [28] M. A. Fischler and R. C. Bolles. RANSAC random sampling concensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of ACM, 26:381–395, 1981. [29] J. Folkesson, P. Jensfelt, and H. Christensen. Vision slam in the measurement subspace. In Proc. IEEE International Conference on Robotics and Automation, Barcelona, Spain, pages 30–35, 2005. [30] W. Förstner and E. Gülch. A fast operator for detection and precise location of distinct points, corners and centres of circular features. In ISPRS Intercommission Workshop, Interlaken, June 1987.
168 [31] F. Fraundorfer and H. Bischof. Detecting distinguished regions by saliency. In Proc. 13th Scandinavian Conference on Image Analysis, Gotenborg, Sweden, pages 208–215, 2003. [32] F. Fraundorfer and H. Bischof. Evaluation of local detectors on non-planar scenes. In Proc. 28th Workshop of the Austrian Association for Pattern Recognition, Hagenberg, Austria, pages 125–132, 2004. [33] F. Fraundorfer and H. Bischof. A novel performance evaluation method of local detectors on non-planar scenes. In Workshop Proceedings Empirical Evaluation Methods in Computer Vision, IEEE Conference on Computer Vision and Pattern Recognition, San Diego, California, 2005. [34] F. Fraundorfer and H. Bischof. Global localization from a single feature correspondence. In Proc. 30th Workshop of the Austrian Association for Pattern Recognition, Obergurgl, Austria, pages 151–160, 2006. [35] F. Fraundorfer, S. Ober, and H. Bischof. Natural, salient image patches for robot localization. In Proc. International Conference an Pattern Recognition, Cambridge, UK, pages 881–884, 2004. [36] F. Fraundorfer, M. Winter, and H. Bischof. Mscc: Maximally stable corner clusters. In Proc. 14th Scandinavian Conference on Image Analysis, Joensuu, Finland, pages 45–54, 2005. [37] F. Fraundorfer, M. Winter, and H. Bischof. Maximally stable corner clusters: A novel distinguished region detector and descriptor. In Proc. 1st Austrian Cognitive Vision Workshop, Zell an der Pram, Austria, pages 59–66, 2005. [38] F. Fraundorfer, K. Schindler, and H. Bischof. Piecewise planar scene reconstruction from sparse correspondences. Image and Vision Computing, 24(4):395–406, 2006. [39] T. Goedeme, M. Nuttin, T. Tuytelaars, and L. Van Gool. Markerless computer vision based localization using automatically generated topological maps. In European Navigation Conference GNSS, Rotterdam, 2004. [40] C. Harris and M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, 1988. [41] R. Hartley. Projective reconstruction from line correspondences. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, Seattle, Washington, pages 903–907, 1994. [42] R. Hartley. Lines and points in three views and the trifocal tensor. International Journal of Computer Vision, 22(2):125–140, 1997. [43] R. Hartley. Theory and practice of projective rectification. International Journal of Computer Vision, 35(2):115–127, 1999. [44] R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge, 2000. [45] J. Hayet, F. Lerasle, and M. Devy. Planar landmarks to localize a mobile robot. In 8th International Symposium on Intelligent Robotic Systems, Reading, UK, pages 163–169, 2000.
Page 1 and 2:
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 .
Page 9 and 10:
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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6.1. Wide-baseline region matching
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6.1. Wide-baseline region matching
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6.2. Piece-wise planar scene recons
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Chapter 7 Living in a piecewise pla
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7.1. Map building 112 s,R,t sub-map
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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
Page 143 and 144: 8.2. Map building experiments 136 (
Page 145 and 146: 8.2. Map building experiments 138 F
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: Bibliography [1] S. Atiya and G. Ha
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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