發(fā)布時間：2018-06-04 12:29

  本文選題：雙目視覺 + 柑橘果實��； 參考：《重慶理工大學》2017年碩士論文

【摘要】：柑橘是我國廣泛種植的水果之一,在柑橘生產(chǎn)作業(yè)中,33%-50%的勞動力用于采摘,采摘作業(yè)重復性強且對勞動力需求巨大。如果能在柑橘生產(chǎn)過程中引進智能采摘機器人,實現(xiàn)機械化、智能化采摘,可以解放有限的勞動力資源,提高生產(chǎn)率,不但具有重要現(xiàn)實意義,也將是農(nóng)業(yè)現(xiàn)代化的必然選擇。果實識別和定位,是采摘機器人的首要任務(wù)和設(shè)計難點,其準確性決定了采摘機器人的工作效果。因此,對柑橘采摘機器人目標識別和定位技術(shù)的研究,具有重要的意義。本論文基于雙目視覺技術(shù),對柑橘采摘機器人目標識別和定位技術(shù)進行了研究,實現(xiàn)了柑橘果實的有效識別和三維空間的定位,為柑橘采摘機器人提供了必要的信息。主要的研究工作與成果如下:(1)搭建了雙目立體視覺系統(tǒng),并進行了雙目攝像機的標定,得到了攝像機的內(nèi)部和外部參數(shù)。對左、右相機進行了立體標定,得到了描述兩個相機空間位置關(guān)系的旋轉(zhuǎn)矩陣和平移向量。(2)對柑橘圖像的分割方法進行了研究,對比了三種不同的柑橘圖像分割方法,選擇K-means聚類算法與HSV顏色空間下閾值分割的分割方法作為本研究的柑橘圖像分割方法,設(shè)計了圖像預(yù)處理方法對分割后的圖像進行進一步的處理。(3)對柑橘圖像的識別方法進行了研究,分別對單個柑橘目標和重疊柑橘目標進行了識別,并提出一種基于凸殼及距離變換理論的重疊柑橘目標識別方法,實現(xiàn)了柑橘的采摘中心點定位和柑橘目標的還原。試驗結(jié)果表明,對于單個柑橘的識別,平均識別誤差為2.03%。對于重疊柑橘目標的識別,仿真試驗中的采摘中心點定位誤差為6.51%,真實重疊柑橘的采摘中心點定位試驗中,本論文方法的定位誤差為1.58%。在重疊柑橘圖像的還原試驗中,平均還原誤差為13.78%,表明該算法能夠較精確地識別柑橘。(4)對柑橘目標的三維空間定位方法進行了研究,論文使用SURF算法進行了特征點提取,利用RANSAC算法和極線約束進行了誤匹配點對的剔除,并依此提出了一種基于柑橘圖像相似度及極線約束的采摘中心點匹配方法,實現(xiàn)了柑橘目標采摘中心點的三維坐標計算。試驗結(jié)果表明,柑橘的平均定位誤差為1.824 mm,滿足采摘機器人定位要求。
[Abstract]:Citrus is one of the fruits widely planted in China. In citrus production, 33% to 50% of the labor force is used for picking. If intelligent picking robot can be introduced into citrus production process, mechanization and intelligent picking can liberate limited labor resources and increase productivity, which is not only of great practical significance, but also an inevitable choice of agricultural modernization. Fruit recognition and location is the most important task and design difficulty of picking robot, and its accuracy determines the working effect of picking robot. Therefore, it is of great significance to study the target recognition and location technology of citrus picking robot. In this paper, based on binocular vision technology, the target recognition and location technology of citrus picking robot is studied, which can effectively recognize citrus fruit and locate in three dimensional space, which provides necessary information for citrus picking robot. The main research work and results are as follows: 1) the binocular stereo vision system is built, and the binocular camera is calibrated, and the internal and external parameters of the camera are obtained. The rotation matrix and translation vector of the two cameras are obtained to describe the spatial relationship between the two cameras. The segmentation methods of citrus images are studied, and three different methods of citrus image segmentation are compared. The K-means clustering algorithm and the threshold segmentation method based on HSV color space are selected as the citrus image segmentation methods in this paper. The image preprocessing method is designed to further process the segmented image. The recognition method of citrus image is studied, and the single citrus target and the overlapping citrus target are identified, respectively. An overlapping citrus target recognition method based on convex hull and distance transformation theory is proposed to locate the picking center point and restore the citrus target. The results show that the average recognition error for single citrus is 2.03. For the identification of overlapping citrus targets, the positioning error of picking center point in simulation experiment is 6.51, and that in real overlapping citrus picking center point positioning test is 1.58. In the experiment of the reduction of overlapping citrus images, the average reduction error is 13.78, which shows that the algorithm can accurately identify the citrus. The 3D spatial location method of citrus target is studied. SURF algorithm is used to extract the feature points. The RANSAC algorithm and polar line constraint are used to eliminate the mismatched point pairs, and a matching method of picking center point based on citrus image similarity and polar line constraint is proposed, which realizes the 3D coordinate calculation of the citrus target picking center point. The experimental results show that the average positioning error of citrus is 1.824 mm, which meets the requirements of picking robot.
【學位授予單位】：重慶理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP242

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本文編號：1977358