Prediction of AR marker's position: A case of study using regression analysis with Machine Learning method
Villegas Hernández, Yazmín Sarahí
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In an automated assembly process with robotic assistance, it is used vision system, which is used to monitor or control the assembly process. In the assembly process, the vision system recognizes objects and estimates the position and orientation. Furthermore, the optical tracking information in manufacturing can provide valuable support and time saving for autonomous operations, but ill environment conditions prevent a better performance of vision systems. This thesis research presents a novel method for estimating object position under semi-controlled environment where lighting conditions change dynamically is proposed. This method incorporates machine learning and regression analysis that combines light measurement and an augmented reality (AR) system. Augmented Reality (AR) combines virtual objects with real environment. Furthermore, every AR application uses a video camera to capture an image including a marker in order to place a virtual object, which gives user an enriched environment. Using a tracking system to estimate the marker’s position with respect to the camera coordinate frame is needed to positioning a virtual object. Most research studies on tracking system for AR are under controlled environment. The problem is that tracking systems for markers are sensitive to variations in lighting conditions in the real environment. To solve this problem, a method is proposed to estimate better a marker position based on regression analysis, where lighting conditions are taken into account. The proposed approach improves the accuracy of the marker position estimation under different lighting conditions.