Pose Estimation of Micro Component Based on 3D Gaussian Splatting

Accurate estimation of the three-dimensional pose of micro component within the assembly space is essential for the efficient operation of micro assembly systems. Existing approaches typically rely on either 2D depth estimation from images or optical measurements, with the former struggling to produce precise 3D models and the latter being unsuitable for cross-scale assembly tasks. In the field of computer vision, mature 3D reconstruction methods for scenes have already been widely applied. In this paper, we use a single microscopic device with a method called 3D Gaussian splatting (3DGS), a 3D reconstruction technique in computer vision, to reconstruct the micro-assembly environment. We use a monocular camera to identify AprilTag markers on the assembly platform, calculate the relative position of the camera and employ 3DGS to reconstruct the 3D microassembly space. We segment the micro component from the point cloud and compute its pose. Experimental results demonstrate that our approach can accurately estimate the relative position of component within the micro assembly space, providing a high-fidelity representation of the assembly environment. Our method lays a data foundation for control algorithms, such as reinforcement learning, by providing a high-fidelity digital model of the micro-assembly space and the precise pose of micro component.