为解决传统分拣机器人通过人工示教完成指定动作其自动化程度低、实时性差、可移植性弱等问题,研究设计了基于机器视觉的分拣机器人实验平台,提出了基于粗配准与增强精配准混合递进配准策略的6D位姿高精度估计方法。首先,将由视觉传感器采集的3D点云信息进行背景信息去除、目标区域裁剪、ROI提取等预处理;然后采用采样一致性初始配准算法(SAC-IA)进行位姿粗配准,再利用迭代最近点算法(ICP)进行精配准及正态分布变换(NDT)进行增强精细配准,以获得高精度6D位姿。实验结果证明,通过粗配准与增强精配准能够快速准确获得待抓取目标6D位置和姿态,与理论位置和姿态相比较其误差分别控制在1.5mm和2°之内,满足分拣机器人的实际需求,所提出的方法便于在基于机器视觉的机器人装配、打磨等有高精度6D位姿估计场合推广应用。
Abstract
In order to solve the problems of low degree of automation, poor real-time performance and weak portability of traditional sorting robots when they complete designated actions through manual instruction, an experimental platform of sorting robots based on machine vision is designed, and a high-precision 6D pose estimation method based on the mixed progressive registration strategy of coarse registration and enhanced fine registration is proposed. Firstly, the 3D point cloud information collected by the vision sensor is preprocessed, such as background information removal, target region clipping, ROI extraction, etc., and the sampling consistency initial registration algorithm (SAC-IA) is used for pose rough registration. Then the iterative closest point (ICP) algorithm is used for fine registration and the normal distribution transformation (NDT) is used to enhance the fine registration to achieve a high precision 6D position. The experimental results show that the integration of coarse registration and enhanced fine registration can quickly and accurately obtain the 6D position and attitude of the target to be captured, with errors controlled within 1.5mm and 2 ° compared to the theoretical position and attitude, respectively, and meet the practical needs of sorting robots. The proposed method can be applied to the machine vision based robot assembly, grinding and other occasions with high precision 6D pose estimation.
关键词
机器视觉;分拣机器人 /
6D位姿 /
增强精配准;SAC-IA算法 /
ICP 算法;NDT
Key words
machine vision /
sorting robot /
6D pose /
enhanced fine registration /
SAC-IA /
ICP algorithm /
NDT
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基金
国家自然科学基金(51675259);江苏省产学研合作项目(BY2022076);江苏省研究生科研与实践创新计划项目(SJCX22_1071)
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