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  本文關(guān)鍵詞：立體視覺系統(tǒng)及其在靜態(tài)尺寸與動態(tài)軌跡測量中的應(yīng)用　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 立體視覺 相機成像 圖像處理 尺寸檢測 軌跡追蹤

【摘要】：現(xiàn)代制造業(yè)的快速發(fā)展對測量技術(shù)的自動化和智能化程度的要求不斷提高。機器視覺具有非接觸、精度高、速度快、靈活、信息豐富等優(yōu)點,在產(chǎn)品的質(zhì)量檢測、機械加工、目標(biāo)追蹤等領(lǐng)域具有重要的研究價值。本文以立體視覺測量系統(tǒng)為研究對象,分析了相機標(biāo)定、圖像采集和預(yù)處理、特征提取與匹配、幾何測量算法等理論問題,構(gòu)建了立體視覺測量系統(tǒng)軟硬件平臺,實現(xiàn)了立體視覺系統(tǒng)在靜態(tài)工件尺寸測量和動態(tài)目標(biāo)空間軌跡信息獲取中的有效應(yīng)用。首先闡述了機器視覺測量技術(shù)國內(nèi)外現(xiàn)狀和發(fā)展趨勢,重點分析了視覺系統(tǒng)的硬件性能指標(biāo)和選型依據(jù),基于Basler工業(yè)相機、Kowa鏡頭、STM32等硬件設(shè)備搭建了立體視覺測量平臺,使用STM32開發(fā)板對工業(yè)相機組進行同步觸發(fā)控制實現(xiàn)了立體視覺圖像采集。分析了相機成像的原理,建立了理想成像和實際畸變模型。深入研究張正友標(biāo)定法,明確了單目標(biāo)定、雙目標(biāo)定、畸變矯正的原理和實現(xiàn)流程,得到由圖像坐標(biāo)向?qū)嶋H坐標(biāo)轉(zhuǎn)換的理論依據(jù)和計算方法。研究了視覺測量技術(shù)相關(guān)的圖像濾波、二值化、幾何特征提取算法,對比Hough算法和最小二乘算法效果,結(jié)合圓度誤差和最小二乘法對圓形特征進行檢測,結(jié)合最小外接矩形和Harris算法對矩形特征頂點亞像素坐標(biāo)進行提取,提出了基于Harris-NCC算法進行角點特征立體匹配優(yōu)化的計算依據(jù)。將研制的立體視覺測量系統(tǒng)應(yīng)用于工件幾何尺寸的機器視覺測試實驗和羽毛球三維空間運動軌跡測量實驗,分析了實驗誤差,明晰了視覺測量系統(tǒng)的性能參數(shù),驗證了所提算法的有效性,同時也為一些先進的立體視覺算法在工業(yè)靜態(tài)測量和動態(tài)目標(biāo)監(jiān)測領(lǐng)域的實用化提供了一種解決方案。
[Abstract]:The rapid development of modern manufacturing industry requires the automation and intelligence of measurement technology. Machine vision has the advantages of non-contact, high precision, fast speed, flexibility, rich information and so on. It has important research value in the field of product quality testing, machining, target tracking and so on. In this paper, the camera calibration, image acquisition and preprocessing are analyzed with stereoscopic vision measurement system as the research object. The software and hardware platform of stereo vision measurement system is constructed based on the theory of feature extraction and matching, geometric measurement algorithm and so on. It realizes the effective application of stereo vision system in static workpiece dimension measurement and dynamic target space track information acquisition. Firstly, the present situation and development trend of machine vision measurement technology at home and abroad are described. The hardware performance index and the selection basis of the vision system are analyzed emphatically, and the stereo vision measurement platform is built based on the hardware equipment such as Basler industrial camera, Kowa lens and STM32. Using STM32 development board to realize the synchronous trigger control of industrial camera group, the stereo vision image acquisition is realized, and the principle of camera imaging is analyzed. The ideal imaging and actual distortion models are established. The calibration method of Zhang Zhengyou is studied in depth. The principle and realization process of single target setting, double target setting and distortion correction are clarified. The theoretical basis and calculation method of the transformation from image coordinates to actual coordinates are obtained, and the image filtering, binarization and geometric feature extraction algorithms related to visual measurement technology are studied. Compared the effect of Hough algorithm and least square algorithm, combined with roundness error and least square method to detect the circular features. The sub-pixel coordinates of the rectangular feature vertices are extracted with the minimum external rectangle and Harris algorithm. The calculation basis of corner feature stereo matching optimization based on Harris-NCC algorithm is put forward. The developed stereo vision measurement system is applied to machine vision testing experiment of geometric dimension of workpiece and badminton. The experiment of motion trajectory measurement in dimensional space. The experimental error is analyzed, the performance parameters of the vision measurement system are clarified, and the validity of the proposed algorithm is verified. It also provides a solution for the application of some advanced stereo vision algorithms in the field of industrial static measurement and dynamic target monitoring.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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本文編號：1401856