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With advancements in technology, deep learning has become increasingly widespread, particularly in fields like robot control, computer vision, and autonomous driving. In these areas, obtaining pose information of target objects, especially their spatial location, is crucial for robot grasping tasks. Although many effective implementations of six degrees of freedom (6D) pose estimation methods based on RGB images exist, challenges in this domain persist. This paper provides a comprehensive review of traditional 6D pose estimation methods, deep learning approaches, and point cloud techniques by analyzing their advantages and disadvantages. It also discusses evaluation metrics and performance on common datasets for 6D pose estimation. Furthermore, the paper offers a theoretical foundation for robot grasping and explores future directions for 6D pose estimation. Finally, it summarizes the current state and development trends of 6D pose estimation, aiming to help researchers better understand and learn about this field. |
