基于點云的噴漆機器人對汽車保險杠識別和位姿估計
發(fā)布時間：2018-09-19 11:40
【摘要】：深度攝像頭的出現(xiàn)和日益完善,獲取物體的三維信息變得方便和快速,點云作為物體三維信息的一種重要表達形式,以點云為背景的計算機視覺近年來得到了發(fā)展。在許許多多的領域,三維視覺有著平面信息無法取代的作用,將機器視覺的應用拓展到了新的領域。本文研究了以噴漆機器人對汽車保險杠的識別和位姿估計相關的問題,噴漆機器人對三維信息的依賴決定了三維視覺的必要性,點云的運用使得噴漆機器人自動完成對零件的識別成為可能,對于計算機視覺這個領域的進一步發(fā)展,也是有著重要的意義。論文提出了三維點云的識別和位姿估計方案,包括點云處理和分割、點云識別、位姿估計三部分。首先決定了獲取點云的設備選擇,選擇kinect作為機器人的視覺硬件,并且人工獲取了各個保險杠的全視角的完成點云。針對實驗各個階段得到的三維點云,利用直通濾波和統(tǒng)計離群點濾波方法除去了比較明顯的噪聲點,然后通過進一步的稀疏濾波手段,獲得了比較適合后序處理的點云密度,并且由于實驗的需要,提出一種特征點區(qū)別濾波方法,對于Thrift特征點周圍的點云保持比較高的密度,離特征點比較遠的部分保留比較稀疏的點云,并設置了對比實驗驗證效果。對全視角點云進行模擬單視角采集,并計算這些單視角點云的視點特征直方圖VFH(Viewpoint Feature Histogram)特征計算,利用這些數(shù)據(jù)訓練主成分分析SVM(Support Vector Machine)分類器。在識別和位姿估計階段,對于濾波處理后的點云數(shù)據(jù),選擇基于最小歐式距離的聚類分割方法實現(xiàn)了對單視角情況下點云數(shù)據(jù)的分割,并對分割后的各個聚類進行提取視點特征直方圖VFH,然后利用已經訓練好的SVM分類器對這些VFH特征進行分類。利用建立kd-tree(kdemention)和BP(Back Propagation)神經網絡識別兩種手段進行了位姿估計并進行對比。其中,識別和位姿估計部分還分別設置了應用主成分分析PCA(Principal Component Analysis)降維和不降維兩種方式的對比試驗。實驗結果表明本論文設計的點云預處理、分割識別和位姿估計具有可行性,能夠更快速的完成識別和位姿估計功能,有較大的探究價值。
[Abstract]:With the appearance and improvement of depth camera, it becomes more and more convenient and fast to obtain 3D information of objects. As an important expression of 3D information of objects, point cloud has been developed in computer vision with point cloud as the background in recent years. In many fields, 3D vision plays an irreplaceable role in plane information, which extends the application of machine vision to new fields. This paper studies the problems related to the identification and pose estimation of automobile bumper by painting robot. The dependence of painting robot on 3D information determines the necessity of 3D vision. The application of the point cloud makes it possible for the painting robot to recognize the parts automatically, which is of great significance to the further development of the field of computer vision. In this paper, three dimensional point cloud recognition and pose estimation schemes are proposed, including point cloud processing and segmentation, point cloud recognition and pose estimation. First, the selection of the device to obtain the point cloud is determined, and the kinect is chosen as the vision hardware of the robot, and the complete point cloud of the full angle of view of each bumper is obtained manually. For the 3D point cloud obtained in each stage of the experiment, the obvious noise points are removed by means of direct pass filtering and statistical outlier filtering, and the point cloud density suitable for post-sequence processing is obtained by further sparse filtering. Because of the need of experiment, a method of distinguishing feature points is proposed, which keeps a high density for the point cloud around the Thrift feature point, and keeps the sparse point cloud in the far part from the feature point, and sets up a comparative experiment to verify the effect. The full view point cloud is simulated and the view feature histogram (VFH (Viewpoint Feature Histogram) of these single view point clouds is calculated. The principal component analysis (SVM (Support Vector Machine) classifier is trained by these data. In the phase of recognition and pose estimation, the minimum Euclidean distance based clustering segmentation method is used to segment the point cloud data with single view angle. The view feature histogram (VFH,) is extracted from each clustering, and then the trained SVM classifier is used to classify these VFH features. Kd-tree (kdemention) and BP (Back Propagation) neural network recognition are used to estimate the position and pose. In the part of recognition and pose estimation, a comparative experiment of using principal component analysis (PCA) to reduce PCA (Principal Component Analysis) and not to reduce dimension is also carried out. The experimental results show that the point cloud preprocessing, segmentation recognition and pose estimation designed in this paper are feasible, and the functions of recognition and pose estimation can be completed more quickly.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP242
本文編號：2250025
[Abstract]:With the appearance and improvement of depth camera, it becomes more and more convenient and fast to obtain 3D information of objects. As an important expression of 3D information of objects, point cloud has been developed in computer vision with point cloud as the background in recent years. In many fields, 3D vision plays an irreplaceable role in plane information, which extends the application of machine vision to new fields. This paper studies the problems related to the identification and pose estimation of automobile bumper by painting robot. The dependence of painting robot on 3D information determines the necessity of 3D vision. The application of the point cloud makes it possible for the painting robot to recognize the parts automatically, which is of great significance to the further development of the field of computer vision. In this paper, three dimensional point cloud recognition and pose estimation schemes are proposed, including point cloud processing and segmentation, point cloud recognition and pose estimation. First, the selection of the device to obtain the point cloud is determined, and the kinect is chosen as the vision hardware of the robot, and the complete point cloud of the full angle of view of each bumper is obtained manually. For the 3D point cloud obtained in each stage of the experiment, the obvious noise points are removed by means of direct pass filtering and statistical outlier filtering, and the point cloud density suitable for post-sequence processing is obtained by further sparse filtering. Because of the need of experiment, a method of distinguishing feature points is proposed, which keeps a high density for the point cloud around the Thrift feature point, and keeps the sparse point cloud in the far part from the feature point, and sets up a comparative experiment to verify the effect. The full view point cloud is simulated and the view feature histogram (VFH (Viewpoint Feature Histogram) of these single view point clouds is calculated. The principal component analysis (SVM (Support Vector Machine) classifier is trained by these data. In the phase of recognition and pose estimation, the minimum Euclidean distance based clustering segmentation method is used to segment the point cloud data with single view angle. The view feature histogram (VFH,) is extracted from each clustering, and then the trained SVM classifier is used to classify these VFH features. Kd-tree (kdemention) and BP (Back Propagation) neural network recognition are used to estimate the position and pose. In the part of recognition and pose estimation, a comparative experiment of using principal component analysis (PCA) to reduce PCA (Principal Component Analysis) and not to reduce dimension is also carried out. The experimental results show that the point cloud preprocessing, segmentation recognition and pose estimation designed in this paper are feasible, and the functions of recognition and pose estimation can be completed more quickly.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP242
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