In order to address the pose estimation challenges encountered in robotic grasping due to object symmetry and occlusions, the problem of six-degree-of-freedom pose estimation was investigated by integrating multi-view RGB-D data. The proposed method was built upon a single view pose estimation framework, in which a 3D keypoints voting network was employed to independently predict 3D keypoints from each viewpoint. Multi-view information was then effectively integrated through confidence scoring, weighted fusion, and manshift clustering, enabling accurate recovery of the target object pose. Experimental results demonstrate that the proposed method significantly improves pose estimation accuracy in symmetric and occluding scenarios. The multi-view fusion strategy effectively enhances stability and robustness of keypoints prediction. Autonomous grasping experiments in tabletop environments achieve notably higher grasp success rates and execution performance. The experiments have shown that the proposed method has good adaptability and application value in complex grasping scenarios, can effectively improve the autonomy of operations in unstructured scenarios, and provides key technical support for solving intelligent assembly operations under complex working conditions, with broad prospects for industrial application.