A strong choice for learning 3D vision in robotics is “Robotics, Vision and Control: Fundamental Algorithms in MATLAB” by Peter Corke. This book is widely respected for its practical approach, combining robotics and computer vision concepts with clear examples. It covers foundational 3D vision topics like camera models, image formation, and geometric transformations, which are critical for tasks such as object recognition, navigation, and manipulation in robots. The MATLAB-based examples provide hands-on code to experiment with algorithms, making it accessible for developers to bridge theory and implementation.
Corke’s book stands out for its focus on real-world robotics applications. For instance, it explains how stereo vision systems work to estimate depth, a key requirement for robots navigating environments or picking objects. The text also dives into visual odometry, which helps robots track their motion using camera data, and SLAM (Simultaneous Localization and Mapping), a core technique for autonomous robots. The author avoids abstract math-heavy explanations, opting instead for intuitive diagrams and code snippets. For example, the chapter on pose estimation demonstrates how to calculate a robot’s position relative to an object using camera images, a common requirement in industrial automation.
While other books like Multiple View Geometry in Computer Vision by Hartley and Zisserman provide deeper mathematical rigor, they are less tailored to robotics. Corke’s work strikes a balance by focusing on practical tools and algorithms directly applicable to robot perception. Developers will appreciate the inclusion of the Robotics Toolbox and Machine Vision Toolbox for MATLAB, which simplify testing concepts like feature matching or 3D reconstruction. If you’re building robots that need to “see” and interact with the world—such as drones, self-driving cars, or robotic arms—this book provides a clear, code-first pathway to implementing 3D vision systems.