SLAMBOOK

 

GitHub - gaoxiang12/slambook-en: The English version of 14 lectures on visual SLAM.

SLAM: Simultaneous Localization and Mapping

SLAM, or Simultaneous Localization and Mapping, is a rapidly growing field in the world of robotics and computer vision. The technology has revolutionized the way robots perceive and navigate their environment, making it a critical component of autonomous systems

 

However, in my case, I found it very difficult to study SLAM because:

1. Complexity of the topic: SLAM involves a combination of various branches of mathematics and computer science, including robotics, computer vision, and machine learning. This can make it challenging for people who are new to the field to understand the concepts and algorithms involved.
2. Mathematical rigor: SLAM requires a strong understanding of mathematical concepts, including linear algebra, probability theory, and optimization. This can be challenging for those who have a weak mathematical background.
3. Abstraction: SLAM is an abstract concept that involves representing the world as a series of mathematical models and algorithms. This abstraction can make it difficult for some people to understand how the technology works in the real world.
4. Lack of practical examples: While there are many theoretical concepts and algorithms associated with SLAM, it can be challenging to understand how they are applied in real-world scenarios. This is why many people find it helpful to study practical examples of SLAM systems.
5. Rapid advancements: The field of SLAM is rapidly evolving, and new technologies and algorithms are being developed all the time. This can make it difficult for people to keep up with the latest developments and can lead to a sense of confusion or frustration.

I have studied SLAM for quite a long time, but I still feel I'm lacking. So I set my goal for this year to complete a book called "SLAMBOOK" and the future postings will be a review of it.

The book provides a detailed explanation of the mathematical theory, models and algorithms used in SLAM starting from basics step by step.

One of the standout features of the book is it focus on practical applications, provides several examples of SLAM systems and real-world applications, including autonomous vehicles, drones, and robotics systems, This not only helps to reinforce the concepts covered in the book, but also procides a deeper understanding of hos SLAM is used in read-world.

Another aspect of the book is the inclusion of example codes using well-known librarie such as OpenCV, Sophus, and other optimization libraries, which makes readers to practice applying SLAM techniques. I think this is a great book to be familiar with SLAM and gain practical experience.