Manipulation strategies of different objects applied to industrial settings - Identification of Objects and 6D Pose Estimation

Abstract

Abstract 6D pose estimation involves determining the three-dimensional position and orientation of an object, which is crucial for industrial automation tasks such as robot manipulation and assembly. Due to limitations such as occlusion, different lighting conditions, and complex geometric shapes, traditional methods based on deep learning often face difficulties in complex industrial environments. This article studies and compares two 6D attitude estimation methods. Firstly, the effectiveness of the YOLO-6D framework in factory environments was explored, which integrates contour-based learning and geometric constraints. Afterwards, the SAM-6D model was attempted for 6D pose estimation, which is a zero-sample method that utilizes the Segment Analysis model (SAM) for instance segmentation and hierarchical geometric inference. For the implementation of the two methods, Blenderproc2 was first used to generate a virtual dataset of factory parts, which is used to collect a set of images in the actual factory environment. Afterwards, the model is trained using a virtual dataset and tested using real images. The results indicate that although YOLO-6D was trained on a synthetic dataset, its performance on real images is poor due to the geometric complexity of the parts and limited model capacity. In contrast, SAM-6D exhibits excellent generalization ability in semantic alignment, appearance consistency, and geometric compatibility, achieving an accuracy of 97% in challenging simulation scenarios. This study emphasizes the importance of SAM- 6D for 6D pose estimation in industrial applications where training data is scarce and object diversity is prevalent. The main contributions include optimizing the computational efficiency of SAM-6D by adjusting GPU-CPU allocation and verifying its robustness to traditional methods. These findings provide a foundation for deploying adaptive attitude estimation systems in dynamic industrial environments.