中圖分類號：TP391文獻標識碼：ADOI:10-19358/j-issn-2097-1788-2024-05-006
引用格式：周劍.一種基于點云實例分割的六維位姿估計方法［J］.網(wǎng)絡安全與數(shù)據(jù)治理，2024，43（5）：42-45，60.

6D pose estimation based on point cloud instance segmentation

Abstract： This paper proposes a method based on the SoftGroup instance segmentation model and Principal Component Analysis (PCA) algorithm for estimating object poses. In the field of industrial automation, visual systems are often equipped on robots and robotic arms to estimate the position of target objects using 2D images. However, in complex scenarios such as stacking and occlusion, the recognition accuracy of 2D images tends to decrease. To accurately and efficiently obtain object positions, this paper fully leverages the high-resolution and high-precision advantages of 3D point cloud data. Firstly, RGB-D images captured by a depth camera are converted into point cloud images. Then, the SoftGroup model is employed to segment the target objects in the point cloud image, and finally, the PCA algorithm is used to obtain the six-dimensional pose of the target. Validation on a self-made dataset shows an average AP of 97.5% for the recognition of three types of objects. The recognition time for a single point cloud image is only 0.73 ms, demonstrating the efficiency and real-time capability of the proposed method. This approach provides a new perspective and solution for scenarios such as robot localization and autonomous grasping of robotic arms, with significant potential for practical engineering applications.

Key words : point cloud data; SoftGroup instance segmentation; 6D pose estimation