To address the problem of low accuracy of pose estimation due to the existence of numerous similar features and non-corresponding points in the point cloud， a global feature pose estimation method based on keypoint distance was proposed. In this method， the global features were constructed using the distances between keypoints， thereby avoiding the influence of similar local features on the accuracy of pose estimation. Meanwhile， to improve the matching speed of global features， a feature matching strategy based on distance comparison table was proposed， so that similarity measurement was carried out on global feature votes through the comparison table， thereby avoiding the interference of non-corresponding points and enhancing the efficiency of finding the correspondences by global features effectively. Finally， these correspondences were subjected to Graph-based Reliability for Outlier Removal （GROR） to eliminate outliers and obtain the transformation pose. Experimental results on four public datasets show that compared with Fast Point Feature Histogram （FPFH）， Signature of Histograms of Orientations （SHOT）， and Binarized Signature of Histograms of Orientations （BSHOT）， the proposed method has the area under the precision-recall curve of the feature matching increased by 116%， 169%， and 137% in average， respectively. Moreover， compared with the original GROR， the proposed method has the rotation error and translation error reduced by 47.38% and 52.43%， respectively.