Introduction
Introduction to Visual SLAM: From Theory to Practice is a comprehensive book that serves as an excellent resource for anyone interested in the field of Visual SLAM. Written by Xiang Gao, Tao Zhang, and other experts in the field, this book covers both the theoretical background and practical aspects of Visual SLAM.
The authors of the book have also made the contents of the book available on GitHub, which includes code examples, datasets, and other resources to help readers understand the material. This is a great resource for anyone who wants to learn more about Visual SLAM. You can find the repository with the keyword, slambook. Here's a link of the english version of the slambook, slambook-en.
GitHub - gaoxiang12/slambook-en: The English version of 14 lectures on visual SLAM.
The English version of 14 lectures on visual SLAM. - GitHub - gaoxiang12/slambook-en: The English version of 14 lectures on visual SLAM.
github.com
What is Visual SLAM?
Visual SLAM (Simultaneous Localization and Mapping) is a crucial technology for various fields such as robotics, autonomous driving, and augmented reality. It is a process that allows a machine to construct a map of an unknown environment while simultaneously determining its own position within the map.Visual SLAM is particularly useful in scenarios where GPS data is not available or is unreliable, such as indoor or underground environments.
Chapters
Here is a brief introduction to each chapter.
Chapter 1. Introduction to SLAM
This chapter provides an overview of the Visual SLAM system and explains how it works. It covers the different types of sensors used in the system and explains how the information is used to build a map and localize the system.
Chapter 2. 3D Rigid Body Motion
This chapter covers the basics of 3D rigid body motion and how it can be represented mathematically. It also explains the different types of transformations that can be used to represent motion.
Chapter 3. Lie Group and Lie Algebra
This chapter provides a detailed explanation of Lie groups and Lie algebra, which are used extensively in Visual SLAM for representing and manipulating motion.
Chapter 4. Cameras and Images
This chapter covers the basics of camera models, image formation, and image distortion. It explains how the intrinsic and extrinsic camera parameters are used in Visual SLAM to determine the position and orientation of the camera.
Chapter 5. Nonlinear Optimization
This chapter provides a comprehensive overview of nonlinear optimization techniques, including the Gauss-Newton method, Levenberg-Marquardt method, and sparse bundle adjustment. It also covers different types of error functions used in optimization.
Chapter 6. Visual Odometry: Part I
This chapter introduces the concept of visual odometry in feature based Visual SLAM, which is the process of estimating the motion of a camera by analyzing consecutive images. It covers different approaches for feature detection, feature matching, and epipolar geometry.
Chapter 7. Visual Odometry: Part II
This chapter covers advanced topics in visual odometry in direct Visual SLAM, such as optical flow and other direct methods.
Chapter 8. Filters and Optimization Approaches: Part I
This chapter covers Kalman filters, bundle adjustment and graph optimization, which are used in Visual SLAM for state estimation and data association.
Chapter 9. Filters and Optimization Approaches: Part II
This chapter covers different optimization approaches, such as pose graph optimization, sliding window filter and optimization.
Chapter 10. Loop Closure
This chapter explains the concept of loop closure, which is the process of detecting and correcting errors in the map. It covers different approaches for loop closure etection and correction.
Chapter 11. Dense Reconstruction
This chapter covers dense reconstruction, which is the process of reconstructing a 3D model of the environment using multiple view. It covers different approaches for dense reconstruction and explains how it can be integrated into Visual SLAM.
Chapter 12. Practice: Stereo Visual Odometry
This chapter is a conclusion part of the book, using the knowledge learned before to actually write a visual odometry program.
Chapter 13. Discussions and Outlook
Next
In the upcoming posts of this series, each chapter of the book will be examined in detail, and the concepts, techniques, and algorithms presented in each chapter will be discussed. The next post in this series will cover the first chapter.
Conclusion
Introduction to Visual SLAM:From Theory to Practice is an excellent resource for individuals interested in learning about SLAM technology, particularly in the field of robotics and autonomous vehicles. The authors have done an exceptional job of presenting the material in an easily comprehensible manner, while also covering some of the more advanced concepts and techniques in SLAM. Whether you are new to the field or an expert, this book is definitely worth reading.
This overview has hopefully provided a good introduction to the book, and invited you to follow along in this series to explore each chapter in more detail.
Series
The links to the related posts will be updated here whenver they are uploaded.
