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Simultaneous Localization and Mapping for Mobile Robots

Book Description

As mobile robots become more common in general knowledge and practices, as opposed to simply in research labs, there is an increased need for the introduction and methods to Simultaneous Localization and Mapping (SLAM) and its techniques and concepts related to robotics. Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods investigates the complexities of the theory of probabilistic localization and mapping of mobile robots as well as providing the most current and concrete developments. This reference source aims to be useful for practitioners, graduate and postgraduate students, and active researchers alike.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
  5. Foreword
  6. Preface
  7. Acknowledgment
  8. Section 1: The Foundations of Mobile Robot Localization and Mapping
    1. Chapter 1: Introduction
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. OVERVIEW
      4. 2. TAXONOMIES FOR THE PROBLEMS
      5. 3. HISTORICAL OVERVIEW
      6. 4. ORGANIZATION OF THE BOOK
    2. Chapter 2: Robotic Bases
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. INTRODUCTION
      4. 2. TURNING MACHINES INTO ROBOTS: ACTUATORS
      5. 3. HOW DOES THE WORLD LOOK TO A ROBOT? SENSORS
      6. 4. PROPRIOCEPTIVE SENSORS: INERTIAL SENSORS
      7. 5. EXTEROCEPTIVE SENSORS: CONTACT AND VERY SHORT-RANGE SENSORS
      8. 6. EXTEROCEPTIVE SENSORS: SINGLE-DIRECTION RANGEFINDERS
      9. 7. EXTEROCEPTIVE SENSORS: TWO-DIMENSIONAL RANGEFINDERS
      10. 8. EXTEROCEPTIVE SENSORS: THREE-DIMENSIONAL RANGE SENSORS
      11. 9. EXTEROCEPTIVE SENSORS: RANGE-ONLY SENSORS
      12. 10. EXTEROCEPTIVE SENSORS: IMAGING SENSORS
      13. 11. EXTEROCEPTIVE SENSORS: AIR ANALYSIS SENSORS
      14. 12. ENVIRONMENTAL SENSORS: ABSOLUTE POSITIONING DEVICES
      15. 13. ENERGY SUPPLY
    3. Chapter 3: Probabilistic Bases
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. INTRODUCTION
      4. 2. HISTORICAL OVERVIEW
      5. 3. PROBABILITY SPACES
      6. 4. RANDOM VARIABLES
      7. 5. THE SHAPE OF UNCERTAINTY
      8. 6. SUMMARIZING UNCERTAINTY
      9. 7. MULTIVARIATE PROBABILITY
      10. 8. TRANSFORMING RANDOM VARIABLES
      11. 9. CONDITIONAL PROBABILITY
      12. 10. GRAPHICAL MODELS
    4. Chapter 4: Statistical Bases
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. INTRODUCTION
      4. 2. IN BETWEEN PROBABILITY AND STATISTICS
      5. 3. ESTIMATORS
      6. 4. PROPERTIES OF ESTIMATORS: USE OF THE SAMPLE
      7. 5. PROPERTIES OF ESTIMATORS: CONVERGENCE TO THE ACTUAL VALUE(S)
      8. 6. PROPERTIES OF ESTIMATORS: UNCERTAINTY (VARIANCE) OF THE ESTIMATOR
      9. 7. CONSTRUCTING ESTIMATORS: CLASSICAL ESTIMATORS
      10. 8. CONSTRUCTING ESTIMATORS: BAYESIAN ESTIMATORS
      11. 9. ESTIMATING DYNAMIC PROCESSES
  9. Section 2: Mobile Robot Localization
    1. Chapter 5: Robot Motion Models
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. INTRODUCTION
      4. 2. CONSTANT VELOCITY MODEL
      5. 3. HOLONOMIC MODEL WITH A DIRECTION AND A DISTANCE
      6. 4. NON-HOLONOMIC MODEL WITH TWO WHEEL ENCODERS
      7. 5. NON-HOLONOMIC MODEL WITH ONE ANGULAR AND ONE WHEEL ENCODER
      8. 6. A BLACK BOX UNCERTAINTY MODEL FOR COMMERCIAL ROBOTS
      9. 7. AN ALTERNATIVE MODEL: THE NO-MOTION MOTION MODEL
      10. 8. IMPROVEMENTS OF THE BASIC KINEMATIC MODELS
    2. Chapter 6: Sensor Models
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. INTRODUCTION
      4. 2. THE BEAM MODEL AND RAY-CASTING
      5. 3. FEATURE SENSORS: PROBABILISTIC MODELS
      6. 4. FEATURE SENSORS: DATA ASSOCIATION
      7. 5. “MAP” SENSORS
    3. Chapter 7: Mobile Robot Localization with Recursive Bayesian Filters
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. INTRODUCTION
      4. 2. PARAMETRIC FILTERS FOR LOCALIZATION
      5. 3. NON-PARAMETRIC FILTERS FOR LOCALIZATION
  10. Section 3: Mapping the Environment of Mobile Robots
    1. Chapter 8: Maps for Mobile Robots
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. INTRODUCTION
      4. 2. EXPLICIT REPRESENTATIONS OF THE SPATIAL ENVIRONMENT OF A MOBILE ROBOT
      5. 3. BAYESIAN ESTIMATION OF GRID MAPS
      6. 4. BAYESIAN ESTIMATION OF LANDMARK MAPS: GENERAL APPROACH
      7. 5. BAYESIAN ESTIMATION OF LANDMARK MAPS: RANGE-BEARING SENSORS
      8. 6. BAYESIAN ESTIMATION OF LANDMARK MAPS: BEARING-ONLY SENSORS
      9. 7. BAYESIAN ESTIMATION OF LANDMARK MAPS: RANGE-ONLY SENSORS
      10. 8. OTHER MAP BUILDING ALGORITHMS
    2. Chapter 9: The Bayesian Approach to SLAM
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. INTRODUCTION
      4. 2. ON-LINE SLAM: THE CLASSICAL EKF SOLUTION
      5. 3. FULL SLAM: THE BASIC RBPF SOLUTION
      6. 4. FULL SLAM: IMPROVED RBPF SOLUTIONS
    3. Chapter 10: Advanced SLAM Techniques
      1. ABSTRACT
      2. CHAPTER GUIDELINE
      3. 1. INTRODUCTION
      4. 2. ESTIMATION AS AN OPTIMIZATION PROBLEM: THE TOPOLOGY OF THE STATE-SPACE
      5. 3. GRAPH SLAM: INTRODUCTION
      6. 4. GRAPH SLAM: OPTIMIZING ON MANIFOLDS
      7. 5. VISUAL SLAM WITH BUNDLE-ADJUSTMENT
      8. 6. TOWARDS LIFELONG SLAM
  11. Compilation of References
  12. About the Authors
  13. Appendix 1: Common SE (2) and SE (3) Geometric Operations
    1. 1. ABOUT GEOMETRIC OPERATIONS AND THEIR NOTATION
    2. 2. OPERATIONS WITH SE(2) POSES
    3. 3. OPERATIONS WITH SE(3) POSES
  14. Appendix 2: Resampling Algorithms
    1. 1. REVIEW OF RESAMPLING ALGORITHMS
    2. 2. COMPARISON OF THE DIFFERENT METHODS
  15. Appendix 3: Generation of Pseudo-Random Numbers
    1. 1. SAMPLING FROM A UNIFORM DISTRIBUTION
    2. 2. SAMPLING FROM A 1-DIMENSIONAL GAUSSIAN
    3. 3. SAMPLING FROM AN N-DIMENSIONAL GAUSSIAN
  16. Appendix 4: Manifold Maps for SO(n) and SE(n)
    1. 1. OPERATOR DEFINITIONS
    2. 2. LIE GROUPS AND LIE ALGEBRAS
    3. 3. EXPONENTIAL AND LOGARITHM MAPS
    4. 4. PSEUDO-EXPONENTIAL AND PSEUDO-LOGARITHM MAPS
    5. 5. ABOUT DERIVATIVES OF POSE MATRICES
    6. 6. SOME USEFUL JACOBIANS
  17. Appendix 5: Basic Calculus and Algebra Concepts
    1. 1. BASIC MATRIX ALGEBRA
    2. 2. THE MATRIX INVERSION LEMMA
    3. 3. CHOLESKY DECOMPOSITION
    4. 4. THE GAUSSIAN CANONICAL FORM
    5. 5. JACOBIAN AND HESSIAN OF A FUNCTION
    6. 6. TAYLOR SERIES EXPANSIONS
  18. Appendix 6: Table Notation