Digital Twin Pose Detection for Mechanical Equipment

This book presents a groundbreaking study in the field of intelligent mining and industrial digital twins, addressing the core challenge of precise pose detection for fully-mechanized mining equipment clusters. It introduces a pioneering theory and methodology driven by multi-source data deduction and reconstruction fusion, offering innovative solutions to longstanding limitations in underground pose monitoring. The book's distinctive value lies in its systematic integration of digital twin modeling, intelligent deduction algorithms, and spatiotemporal evaluation mechanisms – particularly through its novel virtual-physical fusion approach that enables full-pose solution solving and dynamic scene reconstruction despite sensor blind zones and measurement abnormalities. Richly supported by high-precision experimental platform case studies and quantitative evaluation frameworks, this work provides practitioners and researchers with actionable methodologies for achieving real-time, accurate equipment positioning critical for automated mining operations. Its comprehensive coverage of equipment kinematics modeling, multi-sensor fusion techniques, and 4D spatiotemporal analysis makes it an indispensable resource for mining engineers, automation specialists, robotics researchers, and advanced students in industrial digital transformation.