Robotics, Vision and Control: Fundamental Algorithms in MATLAB, Second Edition [Bookshelf]

The second edition of this book covers an impressively broad array of important topics in robotics and computer vision. The book’s presentation style is approachable and engaging, not dense nor intimidating, and the material should be accessible to most upper-level, undergraduate engineering and computer science students. Each chapter combines foundational material, historical vignettes, colorful figures, descriptions of current technology, and software examples to help the reader quickly grasp the context, main ideas, and applications; this approach makes the book an excellent reference source. A nice feature is that, for many sections, it is possible to flip directly to that section and grasp the key ideas without having to read all of the material that comes before it. Due to the breadth of the material covered, this book can be used as a textbook for multiple courses in robotics and computer vision. The text sacrifices some mathematical details and derivations in favor of an example-driven, learn-by-doing treatment. Therefore, the instructor may wish to supplement the book with other sources that go into more detail on the fundamentals. This book is an introduction to both computational inverse problems and uncertainty quantification (UQ) for inverse problems. The text discusses more advanced material on Bayesian methods and UQ, including Markov chain Monte Carlo sampling methods for UQ in inverse problems. Each chapter contains the Matlab code, which implements the algorithms and generates the figures as well as a large number of exercises. This material is intended for graduate students, researchers, and applied scientists. It is appropriate for use with courses on computational inverse problems, Bayesian methods for inverse problems, and UQ methods for inverse problems.