摘要
混合现实(Mixed reality,MR)技术的飞速发展为装配场景中的远程协同任务带来了新的可能。作为MR协同系统的前置环节,实物虚拟化的主要任务是为用户浏览与交互提供可靠的模型支撑与虚实融合关系。现有方法无法同时兼顾装配零件细节高还原度、低虚拟化成本的目标。本文提出了一种基于模板匹配和点云配准原理的零件模型精密恢复方法,针对MR远程协同过程中的任务相关零件,首先通过基于背景差分与八叉树空间检索的背景点云分割以及基于超体素的前景粘连点云分割获得零件点云相关的先验信息来分割模板匹配感兴趣区域ROI,解决了原始LineMod算法抗遮挡性弱、模板比对效率低的问题,完成了对零件点云的识别匹配和位姿粗略估计;然后利用ICP算法进一步优化估计的位姿,求解相关零件在重建点云模型中的精确位姿;最后根据此位姿,用高精度的零件CAD模型替换3D重建点云场景中的零件重建点云模型,最终实现对共享MR协同场景的实物虚拟化。通过对复杂装配场景中的多种零件进行实物虚拟化试验,证明了本文方法能够准确识别零件点云,实现精确的实物虚拟化,在MR远程协同任务中具有重要的实用意义。
The rapid development of mixed reality(MR)technology has opened up new possibilities for remote collaborative tasks in assembly scenarios.As the preparatory work of the MR collaboration system,physical virtualization is the main task of providing reliable model support and the relationship between the virtual and the real for user browsing and interaction.The existing methods cannot take into account the goals of high detail reduction and low virtualization cost of assembly parts at the same time.A precision recovery method of part model based on the principle of template matching and point cloud alignment is proposed to realize the replacement of the reconstructed point cloud model in the original scene using a high precision part CAD model.For the task related parts in the process of MR remote collaboration,firstly,the prior information related to the part point cloud is obtained through the segmentation of background point cloud based on background difference and octree spatial retrieval and the segmentation of foreground adhesive point cloud based on supervoxel to segment the ROI of the region of interest of template matching,which solves the problem of weak occlusion resistance and low template comparison efficiency of the original LineMod algorithm,and the identification matching and rough estimation of the part point cloud poses are completed.The estimated poses were then further optimized using the ICP algorithm to achieve positional recovery of the CAD model in the scene.Through the physical virtualization experiments of various parts in complex assembly scene,it is proved that this method can accurately identify the point cloud of parts and realize accurate physical virtualization,which has important practical value in MR remote collaborative task.
作者
张永建
张翔宇
丁晓
陈雨
白晓亮
ZHANG Yongjian;ZHANG Xiangyu;DING Xiao;CHEN Yu;BAI Xiaoliang(AVIC Chengdu Aircraft Industrial(Group)Co.,Ltd.,Chengdu 610092,China;Northwestern Polytechnical University,Xi’an 710072,China)
出处
《航空制造技术》
CSCD
北大核心
2023年第12期117-127,共11页
Aeronautical Manufacturing Technology
基金
国防基础科研计划资助(JCKY2018205B021)。
