In order to meet the battery replacement demand of electric vehicles， a 6D pose estimation method of battery package locking mechanism based on point cloud segmentation is proposed to solve the positioning problem of locking mechanism during battery package docking in battery swapping station. This method uses YOLOv5 network to segment the point cloud of locking mechanism from the scene， and uses voxel filtering and moving least square fitting to filter and smooth the point cloud. The point cloud labels are predicted by the point cloud segmentation network， and the global semantic feature is added to the Fast Point Feature Histograms （FPFH） feature to make up for the defect that the FPFH has only the local feature of the point cloud. According to this feature， the Random Sample Consensus （RANSAC） rigid point cloud registration is leveraged， and the 6D pose of the locking mechanism point cloud is estimated. Finally， the Iterative Closest Point（ICP） algorithm is used to correct the pose estimation results. The experimental results show that the 6D pose estimation algorithm of locking mechanism based on point cloud segmentation has high accuracy， and can overcome the mismatching caused by environmental noise， and accurately obtain the position and attitude of locking mechanism. The angle error of position and attitude estimation can reach 1.90°， the displacement error can reach 1.4 mm， and the RMSE can reach 1.5 mm， which provides an effective solution for battery docking positioning in battery swapping station.