Computer vision technologies have played a critical role in the advancement of robotics, significantly improving its capabilities and applications. Robots equipped with vision systems can identify obstacles, recognize landmarks, and build detailed maps of their environment, facilitating efficient route planning and movement in complex environments. This capability is critically important for applications such as self-driving cars, where the car is responsible for driving activities, including detecting traffic signs, other cars, and pedestrians, in order to navigate from the route origin to its destination. On the other hand, in industrial environments, robots with vision systems can detect defects in products; it also allows them to perform tasks such as sorting, assembling, and packaging products with high precision.

The book’s primary motivation is “to make robotics, vision, and control algorithms accessible to a wide audience.” The book assumes that the reader has at least an undergraduate level of mathematical knowledge in engineering. It also helps to have intermediate knowledge of programming, especially in Python.

The book has 16 chapters. The first chapter introduces the topics covered in the book and explains the way the book is structured. The following 15 chapters are grouped into five sections that can in turn be grouped into three parts: robotics, computer vision, and vision-based control, which is consistent with the title of the book. The first part on robotics consists of eight chapters and covers topics such as navigation, the kinematics of robot arms, and dynamics and control. This is the part of the book that uses the most mathematics. However, the author does his best to minimize mathematical formalisms, which makes the material accessible and digestible. The second part, five chapters, deals with computer vision. This part presents topics such as image processing, feature extraction, and image formation. Finally, the third part (two chapters) discusses vision-based control issues. At the end there are a series of appendices with introductory mathematics topics.

The author created a GitHub repository with a toolbox in Python. This is interesting because it allows the reader to read the book and review the concepts in a practical way. The code is open source and the reader can modify it and use it for their own projects.

Regarding the physical format, this is a massive book--about 850 pages. Almost each chapter has a section showing the use of the toolbox, a summary of the main concepts, additional readings, and exercises. The book has abundant color figures and “Excurse” sections where some concepts are explained in more detail.

In conclusion, this book can be useful for anyone interested in getting into robotics and computer vision using Python.

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