A Product Pose Tracking Paradigm Based on Deep Points Detection

The paper at hand presents a novel and versatile method for tracking the pose of varying products during their manufacturing procedure. By using modern Deep Neural Network techniques based on Attention models, the most representative points to track an object can be automatically identified using its drawing. Then, during manufacturing, the body of the product is processed with Aluminum Oxide on those points, which is unobtrusive in the visible spectrum, but easily distinguishable from infrared cameras. Our proposal allows for the inclusion of Artificial Intelligence in Computer-Aided Manufacturing to assist the autonomous control of robotic handlers.

Automated summary

The novel method presented in the paper tracks the pose of products during manufacturing using deep neural network techniques based on Attention models. The product body is processed with Aluminum Oxide at specific points for differentiation by infrared cameras. This proposal integrates Artificial Intelligence in Computer-Aided Manufacturing for autonomous control of robotic handlers.