一种基于点云实例分割的六维位姿估计方法

6D pose estimation based on point cloud instance segmentation

提出了一种基于SoftGroup实例分割模型和PCA主成分分析算法来估计物体位姿的方法。在工业自动化领域,通常会为诸如机器人、机械臂配备视觉系统并利用二维图像估算目标物体位置,但当目标物体出现堆叠、遮挡等复杂场景时,对二维图形的识别精度往往有所下降。为准确、高效地获取物体位置,充分利用三维点云数据的高分辨率、高精度的优势:首先将深度相机采集到的RGB-D图像转为点云图,接着利用SoftGroup模型分割出点云图中的目标对象,最后用PCA算法得到目标的六维位姿。在自制工件数据集上进行验证,结果表明对三种工件识别的平均AP高达97.5%,单张点云图识别用时仅0.73 ms,证明所提出的方法具有高效性和实时性,为诸如机器人定位、机械臂自主抓取场景带来了全新的视角和解决方案,具有显著的工程应用潜力。

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.