本文關鍵詞：基于立體視覺的道路場景分割與車輛檢測算法研究　出處：《南京理工大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 立體視覺 U-V視差 霍夫變換 可通行區(qū)域 動態(tài)規(guī)劃 車輛檢測

【摘要】：隨著計算機科學和機器人技術(shù)的飛速發(fā)展,先進駕駛輔助系統(tǒng)(Advanced Driver Assistant System,ADAS)已經(jīng)成為智能車輛的研究熱點,并且廣泛應用于軍事、民用、科研等相關領域。要保證智能車輛在道路上安全行駛,就要識別道路的可通行區(qū)域,也就是避免與道路上的凸障礙物相撞或者陷于凹障礙物中。因此,基于立體視覺的可通行區(qū)域與車輛檢測算法近年來引起了學術(shù)界的關注。本文首先對立體視覺相關領域的研究現(xiàn)狀做了簡單介紹,然后針對車載立體視覺的道路場景分割與車輛檢測,做了以下幾個方面的工作:(1)查閱大量文獻資料,研究立體視覺的相關理論原理,深刻理解攝像機標定、圖像矯正等前期工作,闡述了立體視覺系統(tǒng)的數(shù)學模型、成像原理以及U-V視差圖的構(gòu)造,為本文后期工作打下理論基礎。(2)搭建移動機器人模擬車載立體視覺系統(tǒng),編程實現(xiàn)無線手柄對機器人的移動控制,使其能夠搭載立體相機在室外移動并采集圖像;采用張氏標定法完成攝像機的標定工作,為后續(xù)的車輛檢測算法提供相機的內(nèi)外參數(shù);使用SGBM算法對采集到立體圖像數(shù)據(jù)進行立體匹配,并獲得稠密準確的視差圖;在視差圖基礎上建立U-V視差。(3)在使用標準霍夫變換對二值化V-視差進行道路建模時,我們經(jīng)常會遇到的閾值選取困難以及噪聲干擾的問題。對此本文引入灰度加權(quán)的概念,直接在灰度圖像上提取道路特性曲線,從而避免了上述問題并且提高了算法魯棒性。(4)使用改進動態(tài)規(guī)劃算法檢測可通行區(qū)域。針對傳統(tǒng)動態(tài)規(guī)劃算法檢測中會產(chǎn)生的平滑性問題,本文提出改變算法中的平滑項定義,同時考量前后兩列的匹配代價,提高了檢測準確率;同時對算法提出優(yōu)化以滿足實時性的要求。(5)使用自適應閾值的方法檢測障礙物。利用雙目相機參數(shù)以及障礙物最小高度定義閾值;根據(jù)車輛在U-視差圖中的投影特征添加閾值化約束條件來確定車輛水平位置,再通過逐列掃描過濾視差值的方法確定垂直位置。實驗結(jié)果表明本文提出的方法在實際真實世界的各種常見道路環(huán)境中都能夠區(qū)分障礙物區(qū)域和可通行區(qū)域,并能準確的進行車輛檢測,為智能駕駛輔助系統(tǒng)認知外界環(huán)境奠定了基礎。
[Abstract]:With the rapid development of computer science and robot technology, Advanced Driver Assistant System is an advanced driving aid system. Adas (Intelligent vehicle) has become the research hotspot of intelligent vehicle, and is widely used in military, civil, scientific research and other related fields. To ensure the safe driving of intelligent vehicle on the road, it is necessary to identify the passable area of the road. That is to avoid colliding with a convex obstacle on the road or falling into a concave obstacle. In recent years, the research of passable area and vehicle detection algorithm based on stereo vision has attracted the attention of the academic community. Firstly, this paper briefly introduces the research status in the field of stereo vision. Then aiming at the road scene segmentation and vehicle detection of vehicle stereo vision, we do the following work: 1) consult a lot of literature, study the related theory of stereo vision. The mathematical model of stereo vision system, imaging principle and the construction of U-V parallax map are expounded. For the later work of this paper lay a theoretical foundation. 2) build a mobile robot simulation vehicle stereo vision system, programming to achieve the wireless handle to the robot movement control. Enable it to carry a stereo camera in the outdoor movement and capture images; The camera calibration is completed by using Zhang's calibration method to provide the camera internal and external parameters for the subsequent vehicle detection algorithm. The stereo matching of the collected stereo image data is carried out using SGBM algorithm, and dense and accurate parallax images are obtained. The U-V parallax is built on the basis of the disparity graph.) when the standard Hough transform is used to model the binary V-parallax road. We often encounter the problem of threshold selection and noise interference. In this paper, we introduce the concept of grayscale weighting to extract road characteristic curve directly from gray-scale image. In order to avoid the above problems and improve the robustness of the algorithm. 4) using the improved dynamic programming algorithm to detect the passable area. Aiming at the smoothness problem in the detection of traditional dynamic programming algorithm. In this paper, we propose to change the definition of smoothing terms in the algorithm, and consider the matching cost of the two columns to improve the detection accuracy. At the same time, the algorithm is optimized to meet the real-time requirements. (5) the adaptive threshold is used to detect obstacles. The binocular camera parameters and the minimum height of the obstacle are used to define the threshold. According to the projection feature of the vehicle in the U- parallax graph, a threshold constraint condition is added to determine the horizontal position of the vehicle. The experimental results show that the proposed method can distinguish obstacle areas from passable areas in various common road environments in the real world. And can accurately carry out vehicle detection, for intelligent driving assistance system to understand the external environment laid the foundation.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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