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Mobile Robot Navigation with Intelligent Infrared Image Interpretation

  • William L. FehlmanII
  • Mark K. Hinders

Table of contents

  1. Front Matter
    Pages i-xxix
  2. Pages 25-46
  3. Back Matter
    Pages 271-274

About this book

Introduction

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.

Keywords

Artificial Intelligence Infrared Imaging Mobile Robots Navigation Pattern Classification Performance autonom autonomous robot classification learning mobile robot robot

Authors and affiliations

  • William L. FehlmanII
    • 1
  • Mark K. Hinders
    • 2
  1. 1.Dept. Mathematical ScienceUnited States Military AcademyWest PointUSA
  2. 2.Dept. Applied SciencesCollege of William & MaryWilliamsburgUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84882-509-3
  • Copyright Information Springer London 2009
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-84882-508-6
  • Online ISBN 978-1-84882-509-3
  • Buy this book on publisher's site