PhD Thesis Poppinga: RRT - Jacobs University

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CONTENTS 4 Near Field 3D Navigation with the Hough Transform 65 4.1 Approach and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.1.1 The Hough transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.1.2 Plane Parameterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.1.3 Processing of the Hough Space for Classification . . . . . . . . . . . . . . . 68 4.2 Experiments and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2.1 Hough space examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.2.2 General Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 71 5 Patch Map Data-Structure 79 5.1 Mapping with planar patches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2 Surface Patches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.2.1 Planar Patches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.2.2 Trimesh Patches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.2.3 Other Patch Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.3 Generation of patches from point clouds . . . . . . . . . . . . . . . . . . . . . . . . 82 5.3.1 Trimesh Patches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.3.2 Planar Patches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.4 Patch Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.4.1 Patch Map Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.4.2 Patch Map Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.5 Algorithms on patch maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.5.1 Evasion <strong>RRT</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.5.2 Medial Axis algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.5.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6 3D Roadmaps for Unmanned Aerial Vehicles on Planar Patch Map 113 6.1 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.1.1 Explored Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 6.1.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 6.1.3 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.2.1 Results of <strong>RRT</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.2.2 Results of PRM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6.2.3 Lesumsperrwerk dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 7 Conclusion 131 A Addenda & Errata 133 Bibliography 134 10
List of Figures 1.1 Rugbot at a drill and at ELROB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.2 Rugbot at RoboCup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.3 An indicator for 3D mapping: Collapsed Building at Disaster City training site in Texas 23 2.1 Functional principle of stereo vision . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.2 Wave properties and their measurement . . . . . . . . . . . . . . . . . . . . . . . . 30 2.3 A simple scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.4 Sample scene shot with different (3D) cameras . . . . . . . . . . . . . . . . . . . . 32 2.5 A lab scene at different amplitude thresholds . . . . . . . . . . . . . . . . . . . . . . 33 2.6 Deviation at different distances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.7 Inhomogeneous lighting on white homogeneous surface . . . . . . . . . . . . . . . . 36 2.8 SR errors caused by ambient light . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.9 Amplitude influences range measurement . . . . . . . . . . . . . . . . . . . . . . . 37 2.10 Light scattering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.11 A ghost image of the rod can be seen on both edges of the image (circled). . . . . . . 39 2.12 Errors caused by a moving object (AT=500). . . . . . . . . . . . . . . . . . . . . . . 40 2.13 Reflections, here on a floor, cause the measurement of false distances. . . . . . . . . 40 2.14 An object at various too short distances to the camera, AT 0 . . . . . . . . . . . . . . 41 2.15 Irregular distribution of near-IR light from the camera’s illumination unit . . . . . . . 43 2.16 Three test scenes comparing the proposed method to the standard method . . . . . . 44 2.17 Three test scenes comparing the proposed method to the standard AT method of the SwissRanger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.18 Webcam images for scenes demonstrating the correction of errors other than wraparound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.19 SwissRanger images for scenes demonstrating the correction of errors other than wrap-around . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.20 Different error exclusion criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.21 An example for correcting wrapped-around pixels . . . . . . . . . . . . . . . . . . . 49 3.1 The autonomous version of a Rugbot with some important on-board sensors pointed out 51 3.2 Example range images from the Planar/Round/Holes dataset . . . . . . . . . . . . . 52 3.3 A SwissRanger point cloud from the Arena dataset . . . . . . . . . . . . . . . . . . 53 3.4 Photos from the Arena dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5 Photos of the different types of scenes encountered in the Outdoor 1 dataset. The photos are taken by a webcam which is mounted right next to the 3D sensors. . . . . 54 11
Page 1: Towards Autonomous Navigation for R
Page 4 and 5: ased on RRT. We compare our approac
Page 6 and 7: • Jann Poppinga, Max Pfingsthorn,
Page 9: Contents 1 Introduction 19 1.1 Obst
Page 13 and 14: LIST OF FIGURES 6.3 The lab model t
Page 15 and 16: List of Tables 2.1 Specification of
Page 17 and 18: List of Algorithms 1 General form o
Page 19 and 20: Chapter 1 Introduction Robots have
Page 21 and 22: 1.2 3D Mapping The major problem of
Page 23 and 24: 1.2 3D Mapping Figure 1.3: An indic
Page 25 and 26: 1.3 3D Path Planning 1.3 3D Path Pl
Page 27 and 28: Chapter 2 3D Sensing Mobile robotic
Page 29 and 30: 2.2 Stereo cameras object r f b f d
Page 31 and 32: 2.4 Sonar (a) Distance image (b) Am
Page 33 and 34: 2.5 Case study: The SwissRanger tim
Page 51 and 52: Chapter 3 Datasets In this chapter,
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Page 55 and 56: 3.2 Datasets with actuated LRF (a)
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3.4 Summary Figure 3.13: An overvie
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3.4 Summary Table 3.2: SPATIAL PROP
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Chapter 4 Near Field 3D Navigation
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4.1 Approach and Implementation z U
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4.2 Experiments and results Table 4
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Chapter 5 Patch Map Data-Structure
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5.2 Surface Patches Data set Patch
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Patch Map Data-Structure collision
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Patch Map Data-Structure P 2 P 3 P
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Patch Map Data-Structure (a) Neares
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Patch Map Data-Structure Algorithm
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Patch Map Data-Structure Figure 5.1
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Patch Map Data-Structure path lengt
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3D Roadmaps for Unmanned Aerial Veh
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Conclusion variant of the map data-
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Addenda & Errata (a) Outline on the
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[Birk et al., 2010] Birk, A., Patha
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[Gottschalk, 1997] Gottschalk, S. (
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[Konolige, 1999] Konolige, K. (1999
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[Mustafa, 2004] Mustafa, N. H. (200
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[Rusu et al., 2008a] Rusu, R. B., M
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[Unnikrishnan and Hebert, 2003] Unn
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PhD Thesis Poppinga: RRT - Jacobs University

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