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.
Researchers and practitioners in robotics, machine intelligence, image processing, pattern recognition and computer vision: upper-level undergraduate and graduate students in robotics and sensing
Introduction. Data Fusion and Sensor Integration: State of the Art 1990s. Multi-Source Spatial Fusion Using Bayesian Reasoning. Multi-Sensor Strategies Using Dempster/Shafer Belief Accumulation. Data Fusion Techniques Using Robust Statistics. Recursive Fusion Operators: Desirable Properties and Illustrations. Distributed Data Fusion Using Kalman Filtering. Least-Squares Fusion of Multi-Sensory Data. Fusion of Multi-Dimensional Data Using Regularization. Geometric Fusion: Minimizing Uncertainty Ellipsoid Volumes. Combination of Fuzzy Information in the Framework of Possibility Theory. Data Fusion: A Neural Networks Implementation.
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- © Academic Press 1992
- 12th October 1992
- Academic Press
- eBook ISBN: