SLAM Book (2012)
Title: Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods
Authors: Fernández-Madrigal, J.A. and Blanco, J.L.
Release date: 2012
ISBN: 978-1466621046
Pages: 497
Erratum (last update: Feb 2017)
| Errata location | Says… | Should say… |
| Page 200, eq. (20) |  |
Remove all four minus signs. |
| Page 405 | Equations fpi(a,p) and its Jacobians | Corrected versions are in section 4.2 of this techical report. |
Summary of contents
Part I: The Foundations of Mobile Robot Localization and Mapping
CHAPTER 1: Introduction
CHAPTER 2: Robotic Bases
CHAPTER 3: Probabilistic Bases
CHAPTER 4: Statistical Bases
Part II: Mobile Robot Localization
CHAPTER 5: Robot Motion Models
CHAPTER 6: Sensor Models
CHAPTER 7: Mobile Robot Localization with Recursive Bayesian Filters
Part III: Mapping the Environment of Mobile Robots
CHAPTER 8: Maps for Mobile Robots: Types and Construction
CHAPTER 9: The Bayesian Approach to SLAM
CHAPTER 10: Advanced SLAM techniques
Appendices
APPENDIX I: Common SE(2) and SE(3) Geometric Operations
APPENDIX II: Resampling Algorithms
APPENDIX III: Generation of Pseudo-Random Numbers
APPENDIX IV: Manifold Maps for SO(n) and SE(n)
APPENDIX V: Basic Calculus and Algebra Concepts
Detailed Table of Contents
Foreword (by Gabe Sibley)
Preface
Acknowledgments
Part I: The Foundations of Mobile Robot Localization and Mapping
CHAPTER 1: Introduction
1.1 OVERVIEW
1.2 TAXONOMIES FOR THE PROBLEMS
The Representation of Spatial Knowledge
The Structure and Dynamics of the Environment
The Sensory Apparatus of the Robot
The Motor Apparatus of the Robot
The Previous Knowledge
1.3 HISTORICAL OVERVIEW
1.4 ORGANIZATION OF THE BOOK
REFERENCES
CHAPTER 2: Robotic Bases
2.1 INTRODUCTION
2.2 TURNING MACHINES INTO ROBOTS: ACTUATORS
Legged Robots
Flying Robots
Submarine Robots
Wheeled Robots
2.3 HOW DOES THE WORLD LOOK TO A ROBOT? SENSORS
2.4 PROPRIOCEPTIVE SENSORS: INERTIAL SENSORS
2.5 EXTEROCEPTIVE SENSORS (I): CONTACT AND VERY SHORT-RANGE SENSORS
2.6 EXTEROCEPTIVE SENSORS (II): SINGLE-DIRECTION RANGEFINDERS
Triangulation-Based Proximity Sensors
Pulse-Signal Time of Flight (P-ToF)
Continuous-Wave Time of Flight (C-ToF)
Final Remarks
2.7 EXTEROCEPTIVE SENSORS (III): TWO-DIMENSIONAL RANGEFINDERS
2.8 EXTEROCEPTIVE SENSORS (IV): THREE-DIMENSIONAL RANGE SENSORS
2.9 EXTEROCEPTIVE SENSORS (V): RANGE-ONLY SENSORS
2.10 EXTEROCEPTIVE SENSORS (VI): IMAGING SENSORS
2.11 EXTEROCEPTIVE SENSORS (VII): AIR ANALYSIS SENSORS
2.12 ENVIRONMENTAL SENSORS: ABSOLUTE POSITIONING DEVICES
2.13 ENERGY SUPPLY
REFERENCES
CHAPTER 3: Probabilistic Bases
3.1 INTRODUCTION
3.2 HISTORICAL OVERVIEW
3.3 PROBABILITY SPACES
3.4 RANDOM VARIABLES
3.5 THE SHAPE OF UNCERTAINTY
The Shape of Uncertainty of Discrete R.V.s
The Shape of Uncertainty of Continuous R.V.s
The Shape of Uncertainty of Any R.V. The Likelihood
3.6 SUMMARIZING UNCERTAINTY
Moments of a R.V.
Some Interesting Theorems about Moments
Information and Entropy of a R.V.
3.7 MULTIVARIATE PROBABILITY
Joint Probability and Marginalization
Mutual Independence and Covariances
3.8 TRANSFORMING RANDOM VARIABLES
Preliminaries
Sum of Two Continuous, Independent R.V.s
Linear Combination of Continuous, Independent R.V.s
Product and Division of Continuous, Independent R.V.s
Linear Transformation of Continuous, Unidimensional R.V.s
Linear Transformation of Multivariate R.V.s
The Especial Case of the Chi-Squared Distribution
Approximating Arbitrary Transformations
3.9 CONDITIONAL PROBABILITY
3.10 GRAPHICAL MODELS
Definitions and Taxonomy
Factorizations
Learning and Inference with Bayesian Networks
Conditional Independence in BNs and D-Separation
Marginal Distributions
The Graph of Correlations
REFERENCES
CHAPTER 4: Statistical Bases
4.1 INTRODUCTION
4.2 IN BETWEEN PROBABILITY AND STATISTICS
Almost Sure Convergence or Convergence with Probability One
Convergence in Probability or in Measure
Convergence in Distribution
Convergence in Norm or in Q-Norm
Probabilistic Convergence and the Limit Laws
4.3 ESTIMATORS
4.4 PROPERTIES OF ESTIMATORS (I): USE OF THE SAMPLE
Completeness
Sufficiency
Robustness
4.5 PROPERTIES OF ESTIMATORS (II): CONVERGENCE TO THE ACTUAL VALUE(S)
Consistency
Biasedness
Risk
4.6 PROPERTIES OF ESTIMATORS (III): UNCERTAINTY (VARIANCE) OF THE ESTIMATOR
Minimum Variance
Efficiency
4.7 CONSTRUCTING ESTIMATORS (I): CLASSICAL ESTIMATORS
Efficient Estimators
Minimum Variance, Unbiased Estimators
Best Linear Unbiased Estimators
Maximum Likelihood Estimators
Least Squares Estimators
Estimators Constructed with the Method of Moments
4.8 CONSTRUCTING ESTIMATORS (II): BAYESIAN ESTIMATORS
Minimum Mean Squared Error Estimator (MMSE)
Maximum A Posteriori Estimator (MAP)
Median Estimator (MED)
4.9 ESTIMATING DYNAMIC PROCESSES
REFERENCES
Part II: Mobile Robot Localization
CHAPTER 5: Robot Motion Models
5.1 INTRODUCTION
5.2 CONSTANT VELOCITY MODEL
Kinematic Equations
Probabilistic Motion Model
Application Example
Extension to the 3D Case
5.3 HOLONOMIC MODEL WITH A DIRECTION AND A DISTANCE
Kinematic Equations
Probabilistic Motion Model
5.4 NON-HOLONOMIC MODEL WITH TWO WHEEL ENCODERS
Kinematic Equations
Probabilistic Motion Model
5.5 NON-HOLONOMIC MODEL WITH ONE ANGULAR AND ONE WHEEL ENCODER
Kinematic Equations
Probabilistic Motion Model
5.6 A BLACK-BOX UNCERTAINTY MODEL FOR COMMERCIAL ROBOTS
Kinematic Equations
Probabilistic Motion Model
5.7 AN ALTERNATIVE MODEL: THE NO-MOTION MOTION MODEL
Kinematic Equations
Probabilistic Motion Model
5.8 IMPROVEMENTS OF THE BASIC KINEMATIC MODELS
REFERENCES
CHAPTER 6: Sensor Models
6.1 INTRODUCTION
6.2 THE BEAM MODEL AND RAY-CASTING
6.3 FEATURE SENSORS (I): PROBABILISTIC MODELS
6.4 FEATURE SENSORS (II): DATA ASSOCIATION
The Nearest Neighbor DA Algorithm (NN)
The Joint Compatibility Branch and Bound Algorithm (JCBB)
Mahalanobis Distance vs. Matching Likelihood
6.5 “MAP” SENSORS
Grid Map Matching
Point Map Matching
REFERENCES
CHAPTER 7: Mobile Robot Localization with Recursive Bayesian Filters
7.1 INTRODUCTION
7.2 PARAMETRIC FILTERS FOR LOCALIZATION
The Kalman Filter (KF)
The Extended Kalman Filter (EKF)
The Unscented Transform and the Unscented Kalman Filter (UKF)
7.3 NON-PARAMETRIC FILTERS FOR LOCALIZATION
The Discrete Bayes Filter (DBF)
The Histogram Filter (HF)
The Particle Filter (PF)
REFERENCES
Part III: Mapping the Environment of Mobile Robots
CHAPTER 8: Maps for Mobile Robots: Types and Construction
8.1 INTRODUCTION
8.2 EXPLICIT REPRESENTATIONS OF THE SPATIAL ENVIRONMENT OF A MOBILE ROBOT
Grid Maps
Point-Based Maps
Free-Space Maps
Feature or Landmark Maps
Relational Maps and Topological Maps
Symbolic Maps and Semantic Maps
8.3 BAYESIAN ESTIMATION OF GRID MAPS
8.4 BAYESIAN ESTIMATION OF LANDMARK MAPS (I): GENERAL APPROACH
8.5 BAYESIAN ESTIMATION OF LANDMARK MAPS (II): RANGE-BEARING SENSORS
The Inverse Sensor Model
Recursive Bayesian Estimation
8.6 BAYESIAN ESTIMATION OF LANDMARK MAPS (III): BEARING-ONLY SENSORS
The Inverse Sensor Model
Recursive Bayesian Estimation
8.7 BAYESIAN ESTIMATION OF LANDMARK MAPS (IV): RANGE-ONLY SENSORS
The Inverse Sensor Model
Recursive Bayesian Estimation
8.8 OTHER MAP-BUILDING ALGORITHMS
Point Maps
Continuous Random Markov Fields
Pose Constraint Maps
REFERENCES
CHAPTER 9: The Bayesian Approach to SLAM
9.1 INTRODUCTION
9.2 ON-LINE SLAM: THE CLASSICAL EKF SOLUTION
Algorithm Description
Computational Complexity
Uncertainty and Loop Closures
Critical Analysis
9.3 FULL SLAM (I): THE BASIC RBPF SOLUTION
Algorithm Description: RBPF with the Standard Proposal
Critical Analysis
9.4 FULL SLAM (II): IMPROVED RBPF SOLUTIONS
About Importance Weights
Optimal Proposal Distribution with Landmark Maps (“FastSLAM 2.0”)
Optimal Proposal Distribution with Other Maps
REFERENCES
CHAPTER 10: Advanced SLAM techniques
10.1 INTRODUCTION
Objective 1: Seamless Multisensory Fusion
Objective 2: Robust Detection of Loop Closures
Objective 3: Scalability
10.2 ESTIMATION AS AN OPTIMIZATION PROBLEM: THE TOPOLOGY OF THE STATE SPACE
Background
An Elegant Solution to the Problem of the Topology of the State-Space: Optimizing on the Manifold
A Practical Example
10.3 GRAPH SLAM (I): INTRODUCTION
Framework Overview
A Brief Historical Perspective
10.4 GRAPH SLAM (II): OPTIMIZING ON MANIFOLDS
On-Manifold Sparse Non-linear Least-Squares
Efficiently Building the Sparse Linear System
Related Methods and Recent Developments
10.5 VISUAL SLAM WITH BUNDLE-ADJUSTMENT
The Structure of Bundle Adjustment Problems
Robustness against Outliers
Other Advanced Techniques in Bundle Adjustment
10.6 TOWARDS LIFELONG SLAM
Stability vs. Plasticity
The Vastness and Complexity of the World
REFERENCES
APPENDIX I: Common SE(2) and SE(3) Geometric Operations
I.1 ABOUT GEOMETRIC OPERATIONS AND THEIR NOTATION
I.2 OPERATIONS WITH SE(2) POSES
I.3 OPERATIONS WITH SE(3) POSES
REFERENCES
APPENDIX II: Resampling Algorithms
II.1 REVIEW OF RESAMPLING ALGORITHMS
II.2 COMPARISON OF THE DIFFERENT METHODS
REFERENCES
APPENDIX III: Generation of Pseudo-Random Numbers
III.1 SAMPLING FROM A UNIFORM DISTRIBUTION
III.2 SAMPLING FROM A 1-DIMENSIONAL GAUSSIAN
III.3 SAMPLING FROM AN N-DIMENSIONAL GAUSSIAN
REFERENCES
APPENDIX IV: Manifold Maps for SO(n) and SE(n)
IV.1 OPERATOR DEFINITIONS
IV.2 LIE GROUPS AND LIE ALGEBRAS
IV.3 EXPONENTIAL AND LOGARITHM MAPS
IV.4 PSEUDO- EXPONENTIAL AND PSEUDO- LOGARITHM MAPS
IV.5 ABOUT DERIVATIVES OF POSE MATRICES
IV.6 SOME USEFUL JACOBIANS
Jacobian of the SE(3) Pseudo-Exponential Map 
Jacobian of 
Jacobian of 
Jacobian of 
Jacobian of 
Jacobian of 
REFERENCES
APPENDIX V: Basic Calculus and Algebra Concepts
V.1 BASIC MATRIX ALGEBRA
V.2 THE MATRIX INVERSION LEMMA
V.3 CHOLESKY DECOMPOSITION
V.4 THE GAUSSIAN CANONICAL FORM
V.5 JACOBIAN AND HESSIAN OF A FUNCTION
V.6 TAYLOR SERIES EXPANSIONS
REFERENCES
Index