本文選題：無人車 + 雙目視覺��； 參考：《長(zhǎng)安大學(xué)》2016年碩士論文

【摘要】：無人車是一種能夠通過傳感器感應(yīng)識(shí)別周圍環(huán)境,主動(dòng)地進(jìn)行避障和行駛路徑規(guī)劃的智能汽車。無人車在減少交通事故和交通違規(guī)行為,減輕駕駛員勞動(dòng)強(qiáng)度,以及合理規(guī)劃道路資源的分配使用方面具有很大優(yōu)勢(shì)。無人車對(duì)周圍環(huán)境和障礙物的主動(dòng)感知與監(jiān)測(cè)技術(shù)的研究是發(fā)揮無人車在交通系統(tǒng)中優(yōu)勢(shì)的關(guān)鍵點(diǎn)�；陔p目立體視覺的無人車環(huán)境識(shí)別技術(shù)作為無人車對(duì)周圍環(huán)境障礙物識(shí)別的重要方法已成為研究的重點(diǎn)與熱點(diǎn)。因此,論文基于雙目立體視覺測(cè)量技術(shù)對(duì)無人車行駛中障礙物的定位與跟蹤方法進(jìn)行了研究,主要研究?jī)?nèi)容有:(1)建立可應(yīng)用于無人車的雙目立體視覺障礙物位置測(cè)量模型。通過建立包含畸變系數(shù)的攝像機(jī)針孔模型和由兩個(gè)攝像機(jī)組成的雙目攝像機(jī)系統(tǒng)模型,并結(jié)合無人車坐標(biāo)系統(tǒng)、雙目攝像機(jī)在無人車上的安裝關(guān)系研究提出適用于無人車障礙物測(cè)量的雙目立體視覺障礙物位置測(cè)量模型。(2)搭建并標(biāo)定雙目立體視覺障礙物跟蹤測(cè)試平臺(tái)。使用USB接口攝像機(jī)和安裝架搭建雙目立體測(cè)量試驗(yàn)平臺(tái)并通過經(jīng)典張正友平面標(biāo)定法獲得攝像機(jī)內(nèi)參矩陣和外參矩陣。然后對(duì)雙目立體測(cè)量試驗(yàn)平臺(tái)進(jìn)行立體標(biāo)定得到左右攝像機(jī)位置的旋轉(zhuǎn)、平移矩陣并根據(jù)獲得的參數(shù)和測(cè)量模型求解得出障礙物的三維深度信息矩陣。(3)提出基于深度信息與直方圖反向投影法相結(jié)合的CamShift障礙物跟蹤算法。針對(duì)傳統(tǒng)CamShift跟蹤算法需要手動(dòng)選擇初始搜索位置且障礙物與背景色調(diào)相近時(shí)容易跟丟的缺點(diǎn),研究并實(shí)現(xiàn)基于深度信息的改進(jìn)CamShift障礙物跟蹤算法。(4)測(cè)量雙目立體視覺測(cè)試平臺(tái)的精度并對(duì)論文提出的改進(jìn)跟蹤算法進(jìn)行試驗(yàn)研究和驗(yàn)證。通過測(cè)量給定障礙物的距離試驗(yàn)和實(shí)時(shí)跟蹤特定場(chǎng)景運(yùn)動(dòng)障礙物的試驗(yàn)獲得雙目立體視覺測(cè)試平臺(tái)的測(cè)量精度并驗(yàn)證論文提出的基于深度信息的改進(jìn)CamShift障礙物跟蹤算法的可行性。論文以建立適用于無人車的雙目立體測(cè)量模型、改進(jìn)傳統(tǒng)跟蹤算法并進(jìn)行試驗(yàn)驗(yàn)證為研究思路,研究提出有效的無人車障礙物定位計(jì)算模型和基于深度信息的跟蹤算法,具有重要的理論和實(shí)用價(jià)值。
[Abstract]:Unmanned vehicle (UAV) is a kind of intelligent vehicle which can recognize the surrounding environment and actively avoid obstacles and plan the driving path by sensors. Unmanned vehicles have great advantages in reducing traffic accidents and traffic violations, reducing drivers' labor intensity, and rationally planning the allocation and use of road resources. The research on the active perception and monitoring of the surrounding environment and obstacles is the key point to give full play to the advantages of the unmanned vehicle in the traffic system. Binocular stereo vision based environment recognition technology for unmanned vehicles as an important method to identify obstacles around the environment has become the focus and focus of research. Therefore, based on binocular stereo vision measurement technology, this paper studies the location and tracking method of obstacles in driverless vehicles. The main content of this paper is to establish a binocular stereo vision obstacle position measurement model which can be applied to unmanned vehicles. By establishing the camera pinhole model with distortion coefficient and the binocular camera system model composed of two cameras, and combining with the unmanned vehicle coordinate system, Study on the installation relationship of binocular camera in unmanned vehicle A binocular stereo vision obstacle position measurement model. 2) A binocular stereo vision obstacle tracking and testing platform is established and calibrated. The binocular stereo measurement test platform was built by using USB interface camera and mounting frame, and the camera inner and outer parameter matrices were obtained by the classical calibration method of Zhang Zhengyou plane. Then the binocular stereo measurement test platform is calibrated to get the rotation of the left and right camera position. Based on the translation matrix and the obtained parameters and measurement model, the 3D depth information matrix of the obstacle is obtained. (3) A CamShift obstacle tracking algorithm based on the combination of depth information and histogram reverse projection method is proposed. The traditional CamShift tracking algorithm needs to manually select the initial search position and the obstacles are easily lost when the obstacles are close to the background hue. An improved CamShift obstacle tracking algorithm based on depth information is studied and implemented to measure the accuracy of binocular stereo vision test platform. The improved tracking algorithm proposed in this paper is tested and verified. The measurement accuracy of the binocular stereo vision test platform is obtained by measuring the distance test of a given obstacle and the experiment of tracking the moving obstacle in a specific scene in real time. The improved CamShift obstacle and the improved CamShift obstacle based on depth information proposed in this paper are verified. The feasibility of tracing algorithm. Based on the idea of establishing a binocular stereo measurement model suitable for unmanned vehicle, improving the traditional tracking algorithm and verifying it through experiments, this paper presents an effective obstacle location calculation model and a tracking algorithm based on depth information. It has important theoretical and practical value.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：U463.6

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