PhD Thesis Poppinga: RRT - Jacobs University

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ased on RRT. We compare our approach to the established methods of trimeshes and point clouds and find that it performs an order of magnitude faster on 3D LRF data and also considerably better on sonar data. We also show that this speed advantage does not come at the cost of loss of precision. To this end, we compare the total explorable space on the different map representations and found they only marginally differ. In summary, the Patch Map is proven to be a viable alternative to standard methods that is much more economical with memory. 4
Preface This thesis is based on my work in the Jacobs Robotics Group at Jacobs University Bremen. It would not have been possible without the work, help, collaboration, and inspiration of, with and by (in alphabetical order) Rares Ambrus, Hamed Bastani, Andreas Birk, Heiko Bülow, Winai Chonnaparamutt, Ivan Delchev, Stefan Markov, Mohammed Nour, Yashodan Nevatia, Kaustubh Pathak, Max Pfingsthorn, Ravi Rathnam, Sören Schwertfeger, Todor Stoyanov, and Narūnas Vaškevičius. Furthermore, I would like to thank my wife Danni and my daughter Lina for keeping my spirits up during the stressful days of completing this thesis. Brigitte Dörr provided the invaluable help of proof-reading together with her cousin. Stefan May clarified open questions in optics. Together with my co-authors, I layed the basis for this thesis in the following publications (the order corresponds to the order of their contribution of the thesis): • Jann Poppinga and Andreas Birk. A Novel Approach to Wrap Around Error Correction for a Time-Of-Flight 3D Camera. In Luca Iocchi, Hitoshi Matsubara, Alfredo Weitzenfeld, and Changjiu Zhou, editors, RoboCup 2008: Robot WorldCup XII, Lecture Notes in Artificial Intelligence (LNAI). Springer, 2009, henceforth referred to as [Poppinga and Birk, 2009]. • Jann Poppinga, Andreas Birk, and Kaustubh Pathak. Hough based Terrain Classification for Realtime Detection of Drivable Ground. In Journal of Field Robotics, 25(1-2):67–88, 2008, henceforth referred to as [Poppinga et al., 2008a]. • Andreas Birk, Todor Stoyanov, Yashodhan Nevatia, Rares Ambrus, Jann Poppinga, and Kaustubh Pathak. Terrain Classification for Autonomous Robot Mobility: from Safety, Security Rescue Robotics to Planetary Exploration. In Planetary Rovers Workshop, International Conference on Robotics and Automation (ICRA). IEEE, 2008. • Birk, A., Poppinga, J., Stoyanov, T., and Nevatia, Y. Planetary Exploration in USARsim: A Case Study including Real World Data from Mars. In Iocchi, L., Matsubara, H., Weitzenfeld, A., and Zhou, C., editors, RoboCup 2008: Robot WorldCup XII, Lecture Notes in Artificial Intelligence (LNAI). Springer, . • Narunas Vaskevicius, Andreas Birk, Kaustubh Pathak, and Jann Poppinga. Fast Detection of Polygons in 3D Point Clouds from Noise-Prone Range Sensors. In International Workshop on Safety, Security, and Rescue Robotics (SSRR). IEEE Press, 2007, henceforth referred to as [Vaskevicius et al., 2007]. • Jann Poppinga, Narunas Vaskevicius, Andreas Birk, and Kaustubh Pathak. Fast Plane Detection and Polygonalization in noisy 3D Range Images. In International Conference on Intelligent Robots and Systems (IROS), pages 3378 – 3383, Nice, France, 2008. IEEE Press, henceforth referred to as [Poppinga et al., 2008b]. 5
Page 1: Towards Autonomous Navigation for R
Page 6 and 7: • Jann Poppinga, Max Pfingsthorn,
Page 9 and 10: Contents 1 Introduction 19 1.1 Obst
Page 11 and 12: List of Figures 1.1 Rugbot at a dri
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
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Page 27 and 28: Chapter 2 3D Sensing Mobile robotic
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3.2 Datasets with actuated LRF (a)
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3.2 Datasets with actuated LRF Figu
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3.3 Dataset with sonar: Lesumsperrw
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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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4.2 Experiments and results 4.2.1 H
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4.2 Experiments and results 55 50 O
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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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5.3 Generation of patches from poin
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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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112 Patch Map Data-Structure
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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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