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130626 ||| eng |
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|a 9781848825093
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|a Fehlman, William L.
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| 245 |
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|a Mobile Robot Navigation with Intelligent Infrared Image Interpretation
|h Elektronische Ressource
|c by William L. Fehlman, Mark K. Hinders
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| 250 |
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|a 1st ed. 2009
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| 260 |
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|a London
|b Springer London
|c 2009, 2009
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| 300 |
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|a XXIX, 274 p
|b online resource
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| 505 |
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|a and Overview -- Data Acquisition -- Thermal Feature Generation -- Thermal Feature Selection -- Adaptive Bayesian Classification Model -- Conclusions and Future Research Directions
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| 653 |
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|a Pattern recognition
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| 653 |
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|a Robotics and Automation
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| 653 |
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|a Pattern Recognition
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Robotics
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| 653 |
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|a Automation
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| 700 |
1 |
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|a Hinders, Mark K.
|e [author]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 856 |
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|u https://doi.org/10.1007/978-1-84882-509-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 629.892
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| 520 |
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|a Mobile robots require the ability to make decisions such as "go through the hedges" or "go around the brick wall." Mobile Robot Navigation with Intelligent Infrared Image Interpretation describes in detail an alternative to GPS navigation: a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. The resulting classification model complements an autonomous robot’s situational awareness by providing the ability to classify smaller structures commonly found in the immediate operational environment. The approach described in this book is an application of Bayesian statistical pattern classification where learning involves labeled classes of data (supervised classification), assumes no formal structure regarding the density of the data in the classes (nonparametric density estimation), and makes direct use of prior knowledge regarding an object class’s existence in a robot’s immediate area of operation when making decisions regarding class assignments for unknown objects. The result is a novel classification model which not only displays exceptional performance in characterizing non-heat generating outdoor objects in thermal scenes, but also outperforms the traditional KNN and Parzen classifiers. Mobile Robot Navigation with Intelligent Infrared Image Interpretation will be of interest to researchers and developers of advanced mobile robots in academic, industrial and military sectors. Advanced undergraduates studying robot sensor interpretation, pattern classification or infrared physics will also appreciate this book
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