The book is intended for advanced students in physics, mathematics, computer science, electrical engineering, robotics, engine engineering, and for specialists in computer vision and robotics on the techniques for the development of vision-based robot projects. It focusses on autonomous and mobile service robots for indoor work, and teaches the techniques for the development of vision-based robot projects. A basic knowledge of informatics is assumed, but the basic introduction helps to adjust the knowledge of the reader accordingly. A practical treatment of the material enables a comprehensive understanding of how to handle specific problems, such as inhomogeneous illumination or occlusion. With this book, the reader should be able to develop object-oriented programs and show mathematical basic understanding. Such topics as image processing, navigation, camera types and camera calibration structure the described steps of developing further applications of vision-based robot projects.
Sprache
Verlagsort
Zielgruppe
Für Beruf und Forschung
Für höhere Schule und Studium
Gewicht
ISBN-13
978-3-527-60440-1 (9783527604401)
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Schweitzer Klassifikation
Dr. Stefan Florczyk, Institute for Computer Science, Munich University of Technology
List of Figures. Symbols and Abbreviations. 1 Introduction. 2 Image Processing. 2.1 Color Models. 2.2 Filtering. 2.3 Morphological Image Processing. 2.4 Edge Detection. 2.5 Skeleton Procedure. 2.6 The Segmentation of Image Regions. 2.7 Threshold. 3 Navigation. 3.1 Coordinate Systems. 3.2 Representation Forms. 3.3 Path Planning. 3.4 The Architecture of a Multilevel Map Representation. 3.5 Self localization. 4 Vision Systems. 4.1 The Human Visual Apparatus. 4.2 The Human Visual Apparatus as Model for Technical Vision Systems. 4.3 Camera Types. 5 CAD. 5.1 Constructive Solid Geometry. 5.2 Boundary representation Schema (B rep). 5.3 Approximate Models. 5.4 Hybrid Models. 5.5 Procedures to Convert the Models. 5.6 The Use of CAD in Computer Vision. 5.7 Three dimensional Reconstruction with Alternative Approaches. 6 Stereo Vision. 6.1 Stereo Geometry. 6.2 The Projection of the Scene Point. 6.3 The Relative Motion of the Camera. 6.4 The Estimation of the Fundamental Matrix B. 6.5 Image Rectification. 6.6 Ego motion Estimation. 6.7 Three dimensional Reconstruction by Known Internal Parameters. 6.8 Three dimensional Reconstruction by Unknown Internal and External Parameters. 6.9 Stereo Correspondence. 6.10 Image sequence Analysis. 6.11 Three dimensional Reconstruction from Image Sequences with the Kalman Filter. 7 Camera Calibration. 7.1 The Calibration of One Camera from a Known Scene. 7.2 Calibration of Cameras in Robot vision Systems. 8 Self learning Algorithms. 8.1 Semantic Maps. 8.2 Classificators for Self organizing Neural Networks. 9 OCR. 10 Redundancy in Robot vision Scenarios. 10.1 Redundant Programs for Robot vision Applications. 10.2 The Program. 10.3 The Program Flow. 10.4 Experiment. 10.5 Conclusion. 11 Algorithm Evaluation of Robot vision Systems for Autonomous Robots. 11.1 Algorithms for Indoor Exploration. 11.2 Experiments. 11.3 Conclusion. 12 Calibration for Autonomous Video based Robot Systems. 12.1 Camera Calibration for Indoor Exploration. 12.2 Simple Calibration with SICAST. 12.3 Experiments. 12.4 Conclusion. 13 Redundant Robot vision Program for CAD Modeling. 13.1 New CAD Modeling Method for Robot vision Applications. 13.2 Experiment. 13.3 Conclusion. Bibliography. Index.
