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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment

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  • © 2020

Overview

Authors:
  1. Xiaochun Wang

    1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

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  2. Xiali Wang

    1. School of Information Engineering, Chang’an University, Xi’an, China

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  3. Don Mitchell Wilkes

    1. Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA

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  • Focuses on building bridges between natural and artificial computation
  • Presents an object identification model and two different strategies for online learning
  • Evaluates using a realistic scenario and delivers convincing results

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-xxii
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Overview and Contributions

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 3-10

    3. Developments in Mobile Robot Localization Research

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 11-33

    4. A Computer Vision System for Visual Perception in Unknown Environments

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 35-60

  3. Unsupervised Learning

    1. Front Matter

      Pages 61-61
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    2. Unsupervised Learning for Data Clustering Based Image Segmentation

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 63-84

    3. An Efficient K-Medoids Clustering Algorithm for Large Scale Data

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 85-108

    4. Enhancing Hierarchical Linkage Clustering via Boundary Point Detection

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 109-128

    5. A New Fast K-Nearest Neighbors-Based Clustering Algorithm

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 129-151

    6. An Efficient EMST Algorithm for Clustering Very High-Dimensional Sparse Feature Vectors

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 153-176

  4. Supervised Learning and Semi-supervised Learning

    1. Front Matter

      Pages 177-177
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    2. Supervised Learning for Data Classification Based Object Recognition

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 179-194

    3. A Fast Image Retrieval Method Based on A Quantization Tree

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 195-214

    4. An Efficient Image Segmentation Algorithm for Object Recognition Using Spectral Clustering

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 215-234

    5. An Incremental EM Algorithm Based Visual Perceptual Grouping

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 235-250

  5. Reinforcement Learning

    1. Front Matter

      Pages 251-251
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    2. Reinforcement Learning for Mobile Robot Perceptual Learning

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 253-273

    3. A Developmental Robotic Paradigm for Mobile Robot Navigation in an Indoor Environment

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 275-292

    4. An Automatic Scene Recognition Using TD-Learning for Mobile Robot Localization in an Outdoor Environment

      • Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes
      Pages 293-310
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Keywords

About this book

This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.

Authors and Affiliations

  • School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

    Xiaochun Wang

  • School of Information Engineering, Chang’an University, Xi’an, China

    Xiali Wang

  • Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA

    Don Mitchell Wilkes

About the authors

Xiaochun Wang received her BS degree from Beijing University and the PhD degree from the Department of Electrical Engineering and Computer Science, Vanderbilt University. She is currently an associate professor of School of Software Engineering at Xi’an Jiaotong University. Her research interests are in computer vision, signal processing, and pattern recognition.

Xia Li Wang received the PhD degree from the Department of Computer Science, Northwest University, China, in 2005. He is a faculty member in the Department of Computer Science, Changan University, China. His research interests are in computer vision, signal processing, intelligent traffic system, and pattern recognition.


D. Mitchell Wilkes received the BSEE degree from Florida Atlantic, and the MSEE and PhD degrees from Georgia Institute of Technology. His research interests include digital signal processing, image processing and computer vision, structurally adaptive systems, sonar,as well as signal modeling. He is a member of the IEEE and a faculty member at the Department of Electrical Engineering and Computer Science, Vanderbilt University. He is a member of the IEEE.

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