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Data Fusion in Robotics & Machine Intelligence.

Author: Mongi A Abidi; Rafael C Gonzalez
Publisher: Oxford : Elsevier Science, 1992.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book addresses the techniques for modeling and integration of data provided by different sensors within robotics and knowledge sources within machine intelligence. Leaders in robotics and machine intelligence capture state-of-the-art technology in data sensor fusion and give a unified vision of the future of the field, presented from both the theoretical and practical angles.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
AUTHOR, UNKNOWN.
Data Fusion in Robotics & Machine Intelligence.
Oxford : Elsevier Science, 1992
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Mongi A Abidi; Rafael C Gonzalez
ISBN: 9780323138352 0323138357
OCLC Number: 829460309
Notes: Chapter 11. Geometric Fusion: Minimizing Uncertainty Ellipsoid Volumes.
Description: 1 online resource (559 pages)
Contents: Front Cover; Data Fusion in Robotics and Machine Intelligence; Copyright Page; Table of Contents; Contributors; Preface; Chapter 1. Introduction; Chapter 2. Data Fusion and Sensor Integration: State-of-the-art 1990s; 2.1 Introduction; 2.2 The Role of Multisensor Integration and Fusion; 2.3 Multisensor Integration; 2.4 Multisensor Fusion; 2.5 Object Recognition; 2.6 Multisensor-based Mobile Robots; 2.7 Applications; 2.8 Conclusions; Chapter 3. Multi-source Spatial Fusion Using Bayesian Reasoning; 3.1 Introduction; 3.2 The Occupancy Grid Framework. 3.3 Using Occupancy Grids for Mobile Robot Mapping3.4 Using Occupancy Grids for Robot Navigation; 3.5 Other Applications; 3.6 Conclusions; Chapter 4. Multisensor Strategies Using Dempster-Shafer Belief Accumulation; 4.1 Introduction; 4.2 Illustration of the Problem; 4.3 Object Representation; 4.4 Generating Object Hypotheses; 4.5 Measuring the Ambiguity in a Set of Hypotheses; 4.6 Predicting the Possible Results of Sensing Operations; 4.7 Observable Features; 4.8 Choosing the Best Sensing Strategy; 4.9 Experimental Results; 4.10 Conclusions. Chapter 5. Data Fusion Techniques Using Robust Statistics5.1 Problem Statement; 5.2 Optimal Fusion; 5.3 Location-Parameter Model; 5.4 Uncertainty Classes; 5.5 Statistical Decision Theory; 5.6 Examples of Research Problems; 5.7 Summary of Results; 5.8 Research Directions; Chapter 6. Recursive Fusion Operators: Desirable Properties and Illustrations; 6.1 Introduction; 6.2 Research on Combination Rules; 6.3 Associativity; 6.4 Monotonicity; 6.5 Symmetry; 6.6 Continuity; 6.7 Smoothness; 6.8 Construction of a Fusion Operator; 6.9 Examples; 6.10 Robustness; 6.11 Concluding Remarks. Chapter 7. Distributed Data Fusion Using Kaiman Filtering: A Robotics Application7.1 Introduction; 7.2 Estimation and the Kaiman Filter; 7.3 Centralized Filters; 7.4 A Decentralized Filter; 7.5 Discussion; 7.6 Appendix; Chapter 8. Kinematic and Statistical Models for Data Fusion Using Kaiman Filtering; 8.1 Introduction; 8.2 Translational and Rotational Kinematics; 8.3 Recursive Estimation via Kaiman Filtering; 8.4 Measurement Data Association; 8.5 Tracking with Direction-Only Data; 8.6 Triangulation Position Measurements; Chapter 9. Least-Squares Fusion of Multisensory Data; 9.1 Introduction. 9.2 Transformation of Object Models9.3 Sensory Features; 9.4 Determining Object Pose; 9.5 Experimental Results; 9.6 Summary; Chapter 10. Fusion of Multi-dimensional Data Using Regularization; 10.1 Introduction; 10.2 Trends in Data Fusion; 10.3 Modeling One-Dimensional Sensory Outputs; 10.4 Modeling Two-Dimensional Sensory Outputs; 10.5 Fusion Constraints; 10.6 Fusion of Two Scene Descriptions; 10.7 Parallel Implementation of the 2-D Fusion Algorithm; 10.8 1-D: Fusion of Stereo and Ultrasonic Range; 10.9 2-D: Fusion of Multisensory Contact Data; 10.10 Summary and Conclusions.

Abstract:

This book addresses the techniques for modeling and integration of data provided by different sensors within robotics and knowledge sources within machine intelligence. Leaders in robotics and machine intelligence capture state-of-the-art technology in data sensor fusion and give a unified vision of the future of the field, presented from both the theoretical and practical angles.

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