Intro
Preface
What is This Book Talking About?
How to Use This Book?
Source Code
Targeted Readers
Style
Exercises (Self-test Questions)
Acknowledgments
Contents
Part I Fundamental Knowledge
1 Introduction to SLAM
1.1 Meet ``Little Carrot''
1.1.1 Monocular Camera
1.1.2 Stereo Cameras and RGB-D Cameras
1.2 Classical Visual SLAM Framework
1.2.1 Visual Odometry
1.2.2 Backend Optimization
1.2.3 Loop Closing
1.2.4 Mapping
1.3 Mathematical Formulation of SLAM Problems
1.4 Practice: Basics
1.4.1 Installing Linux
1.4.2 Hello SLAM

1.4.3 Use CMake
1.4.4 Use Libraries
1.4.5 Use IDE
2 3D Rigid Body Motion
2.1 Rotation Matrix
2.1.1 Points, Vectors, and Coordinate Systems
2.1.2 Euclidean Transforms Between Coordinate Systems
2.1.3 Transform Matrix and Homogeneous Coordinates
2.2 Practice: Use Eigen
2.3 Rotation Vectors and the Euler Angles
2.3.1 Rotation Vectors
2.3.2 Euler Angles
2.4 Quaternions
2.4.1 Quaternion Operations
2.4.2 Use Quaternion to Represent a Rotation
2.4.3 Conversion of Quaternions to Other Rotation Representations
2.5 Affine and Projective Transformation

2.6 Practice: Eigen Geometry Module
2.6.1 Data Structure of the Eigen Geometry Module
2.6.2 Coordinate Transformation Example
2.7 Visualization Demo
2.7.1 Plotting Trajectory
2.7.2 Displaying Camera Pose
3 Lie Group and Lie Algebra
3.1 Basics of Lie Group and Lie Algebra
3.1.1 Group
3.1.2 Introduction of the Lie Algebra
3.1.3 The Definition of Lie Algebra
3.1.4 Lie Algebra mathfrakso(3)
3.1.5 Lie Algebra mathfrakse(3)
3.2 Exponential and Logarithmic Mapping
3.2.1 Exponential Map of SO(3)
3.2.2 Exponential Map of SE(3)

3.3 Lie Algebra Derivation and Perturbation Model
3.3.1 BCH Formula and Its Approximation
3.3.2 Derivative on SO(3)
3.3.3 Derivative Model
3.3.4 Perturbation Model
3.3.5 Derivative on SE(3)
3.4 Practice: Sophus
3.4.1 Basic Usage of Sophus
3.4.2 Example: Evaluating the Trajectory
3.5 Similar Transform Group and Its Lie Algebra
3.6 Summary
4 Cameras and Images
4.1 Pinhole Camera Models
4.1.1 Pinhole Camera Geometry
4.1.2 Distortion
4.1.3 Stereo Cameras
4.1.4 RGB-D Cameras
4.2 Images
4.3 Practice: Images in Computer Vision

4.3.1 Basic Usage of OpenCV
4.3.2 Basic OpenCV Images Operations
4.3.3 Image Undistortion
4.4 Practice: 3D Vision
4.4.1 Stereo Vision
4.4.2 RGB-D Vision
5 Nonlinear Optimization
5.1 State Estimation
5.1.1 From Batch State Estimation to Least-Square
5.1.2 Introduction to Least-Squares
5.1.3 Example: Batch State Estimation
5.2 Nonlinear Least-Square Problem
5.2.1 The First and Second-Order Method
5.2.2 The Gauss-Newton Method
5.2.3 The Levernberg-Marquatdt Method
5.2.4 Conclusion
5.3 Practice: Curve Fitting
5.3.1 Curve Fitting with Gauss-Newton