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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.
Contents:
- Introduction to Multisensor Integration
- Multisensor Data Fusion
- Multisensor Fusion in Object Recognition
- Minimal Representation
- Environment and Sensor Models
- Minimal Representation Multisensor Fusion and Model Selection
- Multisensor Fusion Search Algorithms
- Applying the Abstract Framework to Concrete Problems
- Multisensor Object Recognition in Two Dimensions
- Multisensor Object Recognition in Three Dimensions
- Laboratory Experiments
- Discussion of Experimental Results
Readership: Engineers and researchers in robotics and artificial intelligence.
