摘要
针对现有的抓取检测网络需要大量标注数据进行训练且难以适应新物体抓取检测的问题,提出一种适用于堆叠场景下的物体6DoF抓取位姿检测方法.该方法由模型可抓取位姿模板库、待抓物体选择网络和抓取映射3部分组成.首先基于几何外观特征与力封闭原理,在物体模型上生成一组满足力封闭的6DoF抓取位姿,构建可抓取位姿模板库;然后分割原始场景点云,基于可见性、遮挡性和置信度等确定待抓物体;接着根据待抓物体的在场景中的位姿,将模板库中的抓取位姿映射到该物体上;最后选择与待抓物体质心距离最近且无碰撞6DoF抓取位姿,实现稳定抓取.实验在6种类型物体堆叠场景中进行,结果表明,该方法取得96.2%的平均抓取成功率;相较于PointNetGPD,其抓取成功率提升5~20个百分点.
Aiming at the problem that existing grasping detection networks require a large amount of labeled data for training and are difficult to adapt to new object grasping detection,a 6DoF grasping position detection method is proposed for stacked scenes.The method consists of a library of model grasping pose templates,an object selection network,and a grasping mapping.Based on the geometric appearance features and the force closure principle,a set of 6DoF grasping poses satisfying the force closure are generated on the object model to construct a grasping pose template library;then,the original field attraction cloud is segmented,and the objects to be grasped are identified based on the visibility,occlusion,and confidence;then,based on the pose of the objects to be grasped in the scene,the grasping poses in the template library are mapped onto the objects;finally,the nearest and non-colliding 6DoF grasping poses are selected and mapped with the objects to be grasped;and the nearest center-of-mass and non-colliding 6DoF grasping pose is selected.Finally,the closest collision-free 6DoF grasping pose to the center of mass of the object to be grasped is selected to achieve stable grasping.The experiments are conducted in six types of object stacking scenes,and the results show that the method achieves an average success rate of 96.2%,which is 5–20 percentage points higher than that of PointNetGPD.
作者
石敏
侯京召
朱登明
李兆歆
庞家繁
郭诗盛
Shi Min;Hou Jingzhao;Zhu Dengming;Li Zhaoxin;Pang Jiafan;Guo Shisheng(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206;Foresight Research Laboratory,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190;Agricultural Information Technology Research Laboratory,Chinese Academy of Agricultural Sciences,Beijing 100081;Key Laboratory of Agricultural Big Data,Ministry of Agriculture and Rural Affairs,Beijing 100081)
出处
《计算机辅助设计与图形学学报》
北大核心
2025年第9期1632-1642,共11页
Journal of Computer-Aided Design & Computer Graphics
基金
国家重点研发计划(2020YFB1710400)
国家自然科学基金(61972379)
山东省新旧动能转换重大产业攻关项目(2021-55)
2023年苏州市前沿技术研究项目“基于数字孪生技术的元宇宙数字影棚内容创作平台研发”。
关键词
6DoF抓取检测
无抓取标注
堆叠场景
机械臂抓取
位姿检测
6DoF grasping detection
no grasping annotation
stacked scene
robotic arm grasping
position detection
作者简介
石敏(1975-),女,博士,副教授,硕士生导师,CCF会员,主要研究方向为虚拟现实;侯京召(1999-),男,硕士研究生,主要研究方向为抓取检测;通信作者:朱登明(1973-),男,博士,副研究员,硕士生导师,CCF会员,主要研究方向为虚拟现实、计算机图形学,mdzhu@ict.ac.cn;李兆歆(1983-),男,博士,助理研究员,主要研究方向为三维计算机视觉;庞家繁(2001-),男,硕士研究生,主要研究方向为抓取检测;郭诗盛(2001-),男,硕士研究生,主要研究方向为抓取检测.
