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基于边缘特征的板型物体识别定位算法研究
Alternative TitleStudy on the Recognition and Localization Algorithm of Planar Object Based on Boundary Feature
赵银帅1,2
Department光电信息技术研究室
Thesis Advisor吴清潇
ClassificationTP391.41
Keyword形状匹配 点云边缘提取 点对特征 投票策略 位姿估计
Call NumberTP391.41/Z48/2018
Pages66页
Degree Discipline控制工程
Degree Name硕士
2018-05-17
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract工业机器人和机器视觉组成的分拣系统一直是工业实现智能化的研究热点之一。本文以“工业机器人自动化钢板打磨系统”为应用背景,针对工业环境中常见的板型物体,开展了板型物体识别与定位算法研究,针对不同的钢板场景,本文进行了两个研究内容,一个是基于深度图像边缘特征的板型物体识别与定位算法研究,另一个是基于点云边缘点对特征的板型物体识别与定位算法研究。在各自的应用场景中,本文两个算法都实现了钢板的识别与精确定位。针对整齐的层叠板型目标,本文提出了一种基于深度图像边缘特征的板型物体识别与定位方法。本文算法分为两个阶段,离线建模阶段和在线检测阶段,在离线建模阶段,建立余弦值查找表和所有待检测目标的模板;在线检测包括计算梯度图像和基于图像金字塔的搜索。本文通过减少基准模板的边缘点数目、优化旋转角度离散步长以及基于金字塔的匹配搜索策略提高在线检测的效率。另外,本文将该算法成功应用于“工业机器人自动钢板打磨系统”项目中,根据系统在工作现场的运行统计结果得出,该方法不仅其定位精度在项目容忍范围之内,而且满足项目对实时性的要求。针对有一定倾斜角的杂乱钢板,本文在基于投票策略的位姿估计算法基础上,加入了边缘点对特征,提出了一种基于边缘点对特征的板型物体识别与定位方法。该方法分为两个阶段,离线建模阶段和在线识别阶段,在离线建模阶段,对所有钢板进行边缘点提取,对所有满足约束条件的边缘点构建点对特征,并将该点对和其对应的特征保存在哈希表中;在线识别阶段,首先对场景进行平面分割,对所有待识别平面点云提取边缘,利用与离线建模阶段同样的特征描述,对每一个参考点与每个其他的边缘点构建点对特征,然后进行点对特征对齐和位姿估计。另外在匹配过程中使用位姿聚类以及ICP位姿优化进一步提高匹配的准确度与精度。本文在实验室搭建了实验平台,并进行了大量测试实验,验证了该算法对有一定倾斜角的杂乱板型物体识别与定位的有效性。
Other AbstractBin-Picking system containing of industrial robot and machine vision is one of the research hotspots of intelligent industry. In this paper, based on the application background of the automatic grinding system of steel plates with industrial robot,aiming at common planar objects in industrial environment (e.g. steel plates), the recognition and localization algorithm of planar object based on boundary feature are studied. According to different steel scenario, this paper studied the two contents, one is the study on recognition and localization algorithm of planar object based on boundary feature of depth image,another is the study on recognition and localization algorithm of planar object based on boundary feature of point cloud. In their respective application scenarios, both algorithms realize the identification and precise positioning of the steel plate. In this paper, a method for identifying and locating plate objects based on the edge feature of depth image is proposed. The algorithm is divided into two stages: offline modeling stage and online detection stage. In the off-line modeling stage, the cosine value lookup table and the template to be detected are established. Online detection involves the computing gradient images and the searching based on image pyramids. This paper improves the efficiency of online detection by reducing the number of edge points of the base template, optimizing the rotation Angle and searching strategy based on the pyramid. In addition, the algorithm has been successfully applied to the industrial robot automatic plate grinding system. The statistical results of the system at the scene concluded that not only the positioning accuracy of the method is within the scope of project tolerance, but also the method meets the requirement of the project in real time. For the messy steel plate, on the base of the voting-based pose estimation algorithm, this paper proposed an efficient planar object recognition and localization algorithm based on boundary point pair feature. This method can be divided into two stages, the offline modeling stage and the on-line identification stage. In the offline modeling phase, the edge points are extracted for all steel plates in the first place, and then point pair feature is established for every edge point that satisfies the constraint conditions, and the point pair feature with the point pair is stored in a hash table. In the on-line identification stage, plane segmentation is carried out on the scene in the first place, and then edge points are extracted for every plane point cloud. The point pair features are established for every reference point with each other edge point using the same feature description in the offline modeling phase. And then the feature alignment and pose estimation are performed. In addition, the accuracy and precision of the matching are further improved by using the pose clustering and ICP pose optimization in the matching process. In this paper, the experimental platform was set up in the laboratory, and a large number of test experiments were carried out to verify the effectiveness of the algorithm on the identification and positioning of the disorganized object with certain inclination Angle.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/21823
Collection光电信息技术研究室
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
赵银帅. 基于边缘特征的板型物体识别定位算法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2018.
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