Accurate object pose estimation refers to the ability of a robot to determine both the position and orientation of an object, essential for robotics.
A new study led by Associate Professor Phan Xuan Tan aims to enhance the performance of 6D pose estimation algorithms for improved robotic grasping and automation.
This meticulously designed dataset addresses a major gap in robotic grasping research, allowing robots to perform with higher precision and adaptability in real-world environments.
Despite advancements in deep learning, the performance of 6D pose estimation algorithms largely relies on the quality of the data they are trained on.
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