摘要
在航空航天、汽车制造等领域,工业机器依赖高精度的工件识别技术完成自动装配、分拣等关键任务,但在工业现场普遍存在背景复杂、工件形状与纹理相似、多个工件堆叠等情况,导致错误识别。针对上述问题,首先使用双目结构光相机采集高质量三维点云数据,并通过点云预处理有效滤除复杂环境中的背景噪声干扰;其次,提出基于改进超体素的点云过分割算法,通过引入移动最小二乘法构建局部微分几何约束对原始点云进行保形精简,优化超体素聚类过程,提升对噪声的鲁棒性并有效抑制点云粘连;设计一种基于凹凸性判断的多特征自适应超体素融合机制,综合考虑超体素聚类间的凹凸关系、几何特征相似性等多维约束,实现复杂堆叠场景中多类目标的高精度实例分割;提出基于“局部-全局”特征描述序列的支持向量机分类架构,构建多尺度特征级联描述体系,联合刻画目标的局部几何细节与全局形态特征,有效解决小样本下目标混叠导致的误分类问题。最后,搭建实验平台进行算法验证,实验结果表明,该方法显著提升了相似弱纹理工件在复杂堆叠环境下的实例分割精度及分类准确率,分割精度与识别准确率均达到95%以上。
In the domains of aviation,aerospace,automotive manufacturing,and beyond,industrial robots undertake vital tasks such as automated assembly and meticulous workpiece sorting,relying on precise recognition.Yet,in industrial environments,challenges like intricate backgrounds,workpieces with similar shapes and textures,and stacked arrangements often heighten the vulnerability to incorrect recognition.To surmount these challenges,a binocular structured light camera is first used to acquire of high-fidelity three-dimensional point cloud data.A preprocessing algorithm is then deployed to effectively eliminate background interference and mitigate noise within these complex settings.Subsequently,an innovative point cloud over-segmentation algorithm,integrating the moving least squares method to construct local differential geometric constraints,performs shape-preserving simplification on the raw point cloud.This optimizes the supervoxel clustering process,significantly enhancing robustness against noise and effectively suppressing point cloud adhesion.Further,a multi-feature adaptive supervoxel fusion mechanism based on concavity-convexity constraints is designed.This mechanism comprehensively integrates multi-dimensional constraints,including the concavity-convexity relationships between supervoxel clusters and geometric feature similarity,achieving high-precision instance segmentation of multi-class target in complex stacked scenarios.Building upon this foundation,a support vector machine classification architecture driven by“local-global”descriptor sequences is proposed.This architecture constructs a multi-scale cascaded feature description system that jointly characterizes the local geometric details and global morphological features of the targets.This approach effectively solves the misclassification problem caused by stacked targets under small-sample conditions.Finally,an industrial robot platform is devised for algorithm validation.Experimental findings showcase remarkable enhancements in instance segmentation accuracy and classification precision,particularly for similar weak-textured workpieces.The achieved workpiece segmentation accuracy and recognition precision surpass 95%,affirming the algorithm′s efficacy.In the fields of aviation,aerospace,automotive manufacturing,and beyond,industrial robots undertake vital tasks such as automated assembly and meticulous workpiece sorting,relying on precise recognition.Yet,in industrial environments,challenges like intricate backgrounds,workpieces with similar shapes and textures,and stacked arrangements often heighten the vulnerability to incorrect recognition.To surmount these challenges,a binocular structured light camera is first used to acquire of high-fidelity three-dimensional point cloud data.A preprocessing algorithm is then deployed to effectively eliminate background interference and mitigate noise within these complex settings.Subsequently,an innovative point cloud over-segmentation algorithm is proposed,which integrates the moving least squares method to construct local differential geometric constraints.This enables shape-preserving simplification on the raw point cloud,optimizes the supervoxel clustering process,enhances robustness against noise,and effectively mitigates point cloud adhesion.Further,a multi-feature adaptive supervoxel fusion mechanism based on concavity-convexity constraints is designed.This mechanism comprehensively integrates multi-dimensional constraints,including the concavity-convexity relationships between supervoxel clusters and geometric feature similarity,achieving high-precision instance segmentation of multi-class target in complex stacked scenarios.Building upon this foundation,a support vector machine classification architecture driven by"local-global"descriptor sequences is proposed.This architecture constructs a multi-scale cascaded feature description system that jointly characterizes the local geometric details and global morphological features of the targets.This approach effectively solves the misclassification problem caused by stacked targets under small-sample conditions.Finally,an industrial robot platform is devised for algorithm validation.Experimental findings showcase remarkable enhancements in instance segmentation accuracy and classification precision,particularly for similar weak-textured workpieces.The achieved workpiece segmentation accuracy and recognition precision surpass 95%,affirming the effectiveness and robustness of the proposed method.
作者
谢哲欣
李小丽
蒋金
郑高峰
张陈涛
Xie Zhexin;Li Xiaoli;Jiang Jin;Zheng Gaofeng;Zhang Chentao(Pen-Tung Sah Institute of Micro-Nano Science and Technology,Xiamen University,Xiamen 361102,China;Information Technology Department,Quanzhou Vocational College of Economics and Business,Quanzhou 362000,China;Xiamen King Long United Automotive Industry Co.,Ltd.,Xiamen 361023,China)
出处
《仪器仪表学报》
北大核心
2025年第6期139-153,共15页
Chinese Journal of Scientific Instrument
基金
福建省科技重大专项(2024HZ022013)
福建省自然科学基金(2023J01047)项目资助。
关键词
工件识别
三维点云
实例分割
超体素
特征描述
workpiece recognition
3D point cloud
instance segmentation
supervoxel
feature description
