Robotic vision represents the cutting edge of modern computing, combining artificial intelligence, deep learning, and advanced robotics to enable intelligent machines. As universities worldwide pivot from conventional machine learning to robotic vision, this book serves as an essential guide for researchers, educators, and students entering this transformative field.
This comprehensive resource introduces core topics such as humanoid and arm-type robots, robotic image processing, stereo vision, 3D reconstruction, scene understanding, and vision-based control. Advanced algorithms, including Kalman filters, imitation learning, inverse reinforcement learning, diffusion transformers, and multimodal approaches, are explored in depth. Practical applications are seamlessly integrated with theoretical knowledge, offering lab-based exercises and discussions to enhance hands-on learning.
Readers will gain unique insights into robotic navigation and planning, visual servoing, federated learning, and cutting-edge techniques like the “third eye algorithm” and camera retreat. Designed for accessibility, the book assumes no prerequisites beyond foundational courses in machine learning and deep learning, making it suitable for diverse audiences.
With its structured learning approach and emphasis on both foundational principles and emerging innovations, this book is an indispensable tool for mastering robotic vision. Whether readers aim to advance research, develop autonomous systems, or integrate AI-driven robotics into real-world applications, this book provides the knowledge and skills to succeed.
Index 596
Table of contents (8 chapters)
Introduction to Robotic Vision
Pages 1-25
Robotics
Pages 27-64
Image Processing for Robotics
Pages 65-82
Stereo Vision and 3D Reconstruction
Pages 83-99
Deep Learning for Robotic Vision
Pages 101-118
Robotic Perception and Intelligence
Wei Qi Yan
Pages 119-136
Vision-Based Robotic Control
Pages 137-146
Computational Tools for Robotic Vision
Pages 147-159
Back Matter
Pages 161-174
