关键词: 机器视觉, 六自由度位姿估计, 关键点距离, 全局特征, 距离对照表

Abstract: To address the problem that the accuracy of pose estimation was low 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 distances was proposed. This method constructed global features using the distances between keypoints, 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 a distance comparison table was proposed. Similarity measurement was carried out by voting on global features through the comparison table. Thus, while avoiding the interference of non-corresponding points, the efficiency of finding the correspondence of global features was effectively enhanced. Finally, the identified correspondences were subjected to a new outlier removal strategy based on reliability of correspondence graph (GROR) to eliminate outliers, and the transformation pose was obtained. Experiments were conducted on four public datasets. The results show that, compared with the Fast Point Feature Histogram (FPFH), the Signature of Histograms of Orientations (SHOT), and the Binarized Signature of Histograms of Orientations (BSHOT), the area under the precision-recall curve of the feature matching of the proposed method is increased by 116%, 169%, and 137% respectively. Moreover, compared with the original GROR, the rotational error and translational error of the proposed method are reduced by 47.38% and 52.43% respectively.

Key words: machine vision, six degrees of freedom pose estimation, keypoint distance, global feature, distance comparison table