Multisensor Fusion: A Minimal Representation Framework

Multisensor Fusion: A Minimal Representation Framework

Multisensor Fusion: A Minimal Representation Framework

Multisensor Fusion: A Minimal Representation Framework

Hardcover

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Overview

The fusion of information from sensors with different physical characteristics, such as sight, touch, sound, etc., enhances the understanding of our surroundings and provides the basis for planning, decision-making, and control of autonomous and intelligent machines.The minimal representation approach to multisensor fusion is based on the use of an information measure as a universal yardstick for fusion. Using models of sensor uncertainty, the representation size guides the integration of widely varying types of data and maximizes the information contributed to a consistent interpretation.In this book, the general theory of minimal representation multisensor fusion is developed and applied in a series of experimental studies of sensor-based robot manipulation. A novel application of differential evolutionary computation is introduced to achieve practical and effective solutions to this difficult computational problem.

Product Details

ISBN-13: 9789810238803
Publisher: World Scientific Publishing Company, Incorporated
Publication date: 12/15/1999
Series: Series In Intelligent Control And Intelligent Automation , #11
Pages: 336
Product dimensions: 5.90(w) x 8.60(h) x 1.10(d)

Table of Contents


Preface                                            vii  Introduction                                     1   Introduction to Multisensor Integration          17   Multisensor Data Fusion                          25   Multisensor Fusion in Object Recognition         43   Minimal Representation                           57   Environment and Sensor Models                    75   Minimal Representation Multisensor Fusion and    91   Model Selection   Multisensor Fusion Search Algorithms             125   Applying the Abstract Framework to Concrete      151   Problems   Multisensor Object Recognition in Two            155   Dimensions   Multisensor Object Recognition in Three          173   Dimensions   Laboratory Experiments                           203   Discussion of Experimental Results               225   Conclusion                                       237 Appendix A List of Symbols                         247 Appendix B Object-Oriented Software Architecture   251 Appendix C Error Residuals                         257 Appendix D Mixture Distributions                   259 Appendix E Properties of Mixture Representation    263 Size Appendix F Computing Two-Dimensional Pose from     267 Vision and Touch Data Appendix G Computing Three-Dimensional Pose        271 from Grasp Data Appendix H Quaternion Algebra                      275 Appendix I Computing Three-Dimensional Pose        279 from Vision and Touch Data Bibliography                                       291 Index                                              306 
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