Series in Intelligent Control and Intelligent Automation - Vol. 11
MULTISENSOR FUSION
A Minimal Representation Framework
by Rajive Joshi (Real-Time Innovations Inc., USA) & Arthur C Sanderson (Rensselaer Polytechnic Institute, USA)
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
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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.
| 336pp |
Pub. date: Dec 1999 |
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