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Markov Random Field Terrain Classification for Autonomous Robots in Unstructured Terrain

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Markov Random Field Terrain Classification for Autonomous Robots in Unstructured Terrain Fachbereich 4: Informatik Arbeitsgruppe Aktives Sehen MARKOV RANDOM FIELD TERRAIN CLASSIFICATION FOR AUTONOMOUS ROBOTS IN UNSTRUCTURED TERRAIN Vom Promotionsausschuss des Fachbereichs 4: Informatik an der Universität Koblenz-Landau zur Verleihung des akademischen Grades Doktor der Naturwissenschaften (Dr. rer. nat.) genehmigte DISSERTATION von Dipl.-Inform. Marcel Häselich Koblenz - 2014 Datum der Einreichung: 27. Juni 2014 Datum der wissenschaftlichen Aussprache: 17. Dezember 2014 Vorsitzender des Promotionsausschusses: Prof. Dr. Ralf Lämmel Vorsitzender der Promotionskommision: Prof. Dr. Ulrich Furbach Erster Berichterstatter: Prof. Dr. Dietrich Paulus Zweiter Berichterstatter Prof. Dr. Marcin Grzegorzek Veröffentlicht als Dissertation der Universität Koblenz-Landau. Zusammenfassung Diese Doktorarbeit beschäftigt sich mit dem Problem der Terrainklassifikation im unstrukturierten Außengelände. Die Terrainklassifikation umfasst dabei das Erkennen von Hindernissen und flachen Bereichen mit der einhergehenden Anal- yse der Bodenoberfläche. Ein 3D Laser-Entfernungsmesser wurde als primärer Sensor verwendet, um das Umfeld des Roboters zu vermessen. Zunächst wird eine Gitterstruktur zur Reduktion der Daten eingeführt. Diese Datenrepräsentation ermöglicht die Integration mehrerer Sensoren, z.B. Kameras für Farb- und Textur- informationen oder weitere Laser-Entfernungsmesser, um die Datendichte zu er- höhen. Anschließend werden für alle Terrainzellen des Gitters Merkmale berech- net. Die Klassifikation erfolgt mithilfe eines Markov Zufallsfeldes für Kontextsen- sitivität um Sensorrauschen und variierender Datendichte entgegenzuwirken. Ein Gibbs-Sampling Ansatz wird zur Optimierung eingesetzt und auf der CPU sowie der auf GPU parallelisiert um Ergebnisse in Echtzeit zu berechnen. Weiterhin werden dynamische Hindernisse unter Verwendung verschiedener State-of-the-Art Techniken erkannt und über die Zeit verfolgt. Die berechneten Informationen, wohin sich andere Verkehrsteilnehmer bewegen und in Zukunft hinbewegen kön- nten, werden verwendet, um Rückschlüsse auf Bodenoberflächen zu ziehen die teilweise oder vollständig unsichtbar für die Sensoren sind. Die Algorithmen wurden auf unterschiedlichen autonomen Roboter-Plattformen getestet und eine Evaluation gegen von Menschen annotierte Grundwahrheiten von Karten aus mehreren Millionen Messungen wird präsentiert. Der in dieser Arbeit entwick- elte Ansatz zur Terrainklassifikation hat sich in allen Anwendungsbereichen be- währt und neue Erkenntnisse geliefert. Kombiniert mit einem Pfadplanungsal- gorithmus ermöglicht die Terrain Klassifikation die vollständige Autonomie für radgetriebene Roboter in natürlichem Außengelände. Abstract This thesis addresses the problem of terrain classification in unstructured outdoor environments. Terrain classification includes the detection of obstacles and pass- able areas as well as the analysis of ground surfaces. A 3D laser range finder is used as primary sensor for perceiving the surroundings of the robot. First of all, a grid structure is introduced for data reduction. The chosen data representation allows for multi-sensor integration, e.g., cameras for color and texture information or further laser range finders for improved data density. Subsequently, features are computed for each terrain cell within the grid. Classification is performed with a Markov random field for context-sensitivity and to compensate for sensor noise and varying data density within the grid. A Gibbs sampler is used for opti- mization and is parallelized on the CPU and GPU in order to achieve real-time performance. Dynamic obstacles are detected and tracked using different state-of- the-art approaches. The resulting information — where other traffic participants move and are going to move to — is used to perform inference in regions where the terrain surface is partially or completely invisible for the sensors. Algorithms are tested and validated on different autonomous robot platforms and the eval- uation is carried out with human-annotated ground truth maps of millions of measurements. The terrain classification approach of this thesis proved reliable in all real-time scenarios and domains and yielded new insights. Furthermore, if combined with a path planning algorithm, it enables full autonomy for all kinds of wheeled outdoor robots in natural outdoor environments. Acknowledgments A thesis is a complex project over a long period of one’s life. Mine covered almost half a decade and I owe thanks to a lot of people who accompanied and supported me during that time. I was only able to reach my goal with their support. First of all my thanks goes to the person who made all this possible, my supervisor Prof. Dr.-Ing. Dietrich Paulus. He granted me all the freedom I needed to perform my research while his advice and ideas were equally of great help for this thesis. Secondly, I would like to thank Prof. Dr.-Ing. Marcin Grzegorzek for his will- ingness to mentor my work. Never will I forget that one week in Poland, my first scientific excursion ever, where the Markov random fields idea was forged in the first place. I would also like to express my sincere thanks to all my colleagues. Most notably Frank Neuhaus and Nicolai Wojke who suffered the most from my life- long struggle with cmake and my proclivity for making the same mistakes again. Christian Winkens for sharpening the idea of integrating dynamic obstacles into terrain classification during a workshop in France. Detlev Droege for letting us turn his car into a robot so many times. And of course Dagmar Lang, for her critical eye and excellent reviewing skills. I would like to thank Nicolai a second time, this time for his great work in car detection and tracking. I as well owe a lot of thanks to students that contributed to this thesis: Denis Dillenberger and Frank Neuhaus for putting all the sensors and the platform into operation, and preparing all the stuff I used so frequently later on. Marc Arends for his pioneering work with Markov random fields. Nikolay Handzhiyski and Laura Haraké for their contribution to the robot’s path planning. René Bing for creating the calibration pattern and the useful calibration graphical user interface. Michael Klostermann, Martin Prinzen, and Benedikt Jöbgen for their contribution to the pedestrian detection and tracking system. Furthermore, I would like to thank the employees of the Wehrtechnische Dienststelle 51 in Koblenz for the long-lasting research and development cooperation. Also, my thanks go to Simon Eggert and Benjamin Knopp for always keeping the coffee flowing when fatigue threatened to overwhelm me. And of course my parents: studying computer sciences would not have been possible without their support and financial assistance. Finally, I am grateful to my wife Sabrina, for giving birth to our two wonderful children. Thank you for all the support you gave me and for enduring my moods before important deadlines. Contents 1 Introduction 1 1.1 Motivation............................... 5 1.2 OwnContribution........................... 7 1.3 Outline................................. 9 2 Fundamentals of Markov Random Fields 11 2.1 SitesandLabels............................ 12 2.2 TheLabelingProblem ........................ 12 2.3 NeighborhoodsandCliques . 13 2.4 MarkovRandomFields. 15 2.5 GibbsRandomFields......................... 17 2.6 The Hammersley-Clifford Theorem . 18 2.7 TheIsingandPottsModels . 19 2.8 Probabilistic Graphical Models . 22 2.9 Optimization and Gibbs Sampling . 23 2.10 TheMahalanobisDistance . 27 3 Software Architecture 31 4 Markov Random Field Classification 35 4.1 RelatedWork ............................. 36 4.2 CameraandLaserDataFusion . 39 4.2.1 RelatedWork ......................... 40 4.2.2 Camera Calibration and Camera to Laser Calibration . 41 4.3 MarkovRandomFieldApplication . 46 4.3.1 TerrainDataRepresentation . 46 4.3.2 TerrainFeatures . .. .. 47 4.3.3 Acquisition of Terrain Features . 48 4.3.4 MarkovModeloftheTerrain . 48 9 10 CONTENTS 4.4 EvaluationandOptimization . 51 4.4.1 Gibbs Sampler Optimization . 55 4.5 EgomotionEstimation . 61 4.6 Evaluationon3DMaps ....................... 64 4.7 Spline Templates as Proof of Concept . 72 5 Detection and Tracking of Moving Objects 75 5.1 Detection and Tracking of Vehicles . 75 5.1.1 RelatedWork ......................... 76 5.1.2 SensorDataInterpretation . 78 5.1.3 ForegroundSeparation . 79 5.1.4 Vehicle Detection and Tracking . 82 5.1.5 EgomotionEstimation . 83 5.1.6 Evaluation........................... 84 5.2 Detection and Tracking of Pedestrians . 85 5.2.1 RelatedWork ......................... 86 5.2.2 Pedestrian Detection in Camera Images . 88 5.2.3 Pedestrian Detection and Tracking in Laser Data . 95 6 Integration of Dynamic Obstacles 103 6.1 TrailsandExtrapolation . 104 6.2 IntegrationintotheMarkovRandomField . 107 6.3 Experiments.............................. 109 7 Conclusion 113 Appendices 117 A Data Sheet of the Velodyne HDL-64E 117 B Data Sheet of the Velodyne HDL-32E 121 C Notation Similarities and Differences 125 D Haralick Features 127 List of Abbreviations 129 List of Notations 131 List of Tables 133 List of Figures 135 Own Publications 137 CONTENTS 11 Bibliography 139 Internet resources 159 Chapter 1 Introduction Autonomous navigation and exploration in unstructured environments is a major challenge in robotics and an active field of research. In order to fulfill complex outdoor tasks, a robot needs enhanced sensing and interpreting abilities to detect drivable regions and to avoid collisions. For an autonomous outdoor system,
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