【摘要】：移動(dòng)機(jī)器人視覺導(dǎo)航過程中圖像處理的關(guān)鍵問題是道路識(shí)別和障礙物檢測(cè),論文是基于計(jì)算機(jī)單目視覺技術(shù)對(duì)非結(jié)構(gòu)化道路識(shí)別和運(yùn)動(dòng)障礙物檢測(cè)進(jìn)行研究。在已有的技術(shù)基礎(chǔ)上,經(jīng)分析和實(shí)驗(yàn),本文采用彩色道路邊緣檢測(cè)并結(jié)合道路區(qū)域識(shí)別技術(shù),對(duì)校園環(huán)境下的非結(jié)構(gòu)化道路識(shí)別能夠取得較好的結(jié)果,算法對(duì)復(fù)雜的野外道路環(huán)境也有一定的識(shí)別效果;對(duì)于場(chǎng)景中障礙物檢測(cè)技術(shù),本文采用三幀差分法結(jié)合金字塔光流法,能夠?qū)Φ缆分械倪\(yùn)動(dòng)目標(biāo)進(jìn)行檢測(cè),判定其在圖像中的位置。本文研究內(nèi)容主要分為四個(gè)方面:移動(dòng)機(jī)器人軟件系統(tǒng)設(shè)計(jì)、圖像道路區(qū)域識(shí)別、道路邊緣檢測(cè)、障礙物檢測(cè)。移動(dòng)機(jī)器人軟件系統(tǒng)設(shè)計(jì)方面,本文主要研究的是移動(dòng)機(jī)器人視覺導(dǎo)航系統(tǒng)的設(shè)計(jì)。視覺導(dǎo)航系統(tǒng)的作用是:把從傳感器獲取到的場(chǎng)景信息流經(jīng)過系統(tǒng)中道路識(shí)別、障礙物檢測(cè)、運(yùn)動(dòng)決策等模塊的綜合處理來完成導(dǎo)航任務(wù)。視覺導(dǎo)航系統(tǒng)的設(shè)計(jì)是通過模塊化、多線程等方法來完成,以便系統(tǒng)具有較好的可維護(hù)性和實(shí)時(shí)性。道路區(qū)域識(shí)別方面,本文回顧了道路識(shí)別常用算法類型,詳細(xì)介紹了基于區(qū)域的道路識(shí)別,本文使用顏色特征結(jié)合LBP紋理特征作為圖像總特征,采用監(jiān)督的訓(xùn)練方式,使用二分類效果優(yōu)良的SVM算法訓(xùn)練分類器。用訓(xùn)練好的分類器對(duì)圖像道路區(qū)域進(jìn)行粗識(shí)別。在道路邊緣檢測(cè)方面,本文分析了5種常用的邊緣檢測(cè)算子在道路識(shí)別中的效果,并結(jié)合實(shí)驗(yàn)分析,采用了一種彩色空間下改進(jìn)的Canny邊緣檢測(cè)算法。改進(jìn)的Canny邊緣檢測(cè),不同于傳統(tǒng)算法在2x2的鄰域內(nèi)計(jì)算兩個(gè)方向均值差分,本文采用3x3的4個(gè)方向的部分插值,同時(shí)由于移動(dòng)機(jī)器人場(chǎng)景的不斷變化,本文沒有使用傳統(tǒng)方法的固定閾值,而是根據(jù)每幅圖像采用最大類間方差的自適應(yīng)閾值。改進(jìn)后的Canny邊緣檢測(cè)算法更適合移動(dòng)機(jī)器人自主導(dǎo)航場(chǎng)景。在障礙物檢測(cè)檢測(cè)方面,本文主要研究移動(dòng)機(jī)器人導(dǎo)航過程中的運(yùn)動(dòng)障礙物的檢測(cè)。本文詳細(xì)的介紹了障礙物檢測(cè)常用的三種算法:背景模型差分法、兩幀差分法、光流法。然后依據(jù)移動(dòng)機(jī)器人的硬件資源,在滿足實(shí)時(shí)和準(zhǔn)確性的前提下,使用一種基于三幀差分結(jié)合金字塔光流的障礙物檢測(cè)算法,能實(shí)時(shí)的檢測(cè)到移動(dòng)機(jī)器人前方場(chǎng)景中的運(yùn)動(dòng)目標(biāo)。
[Abstract]:The key problem of image processing in vision navigation of mobile robot is road recognition and obstacle detection. This paper studies unstructured road recognition and moving obstacle detection based on computer monocular vision technology. On the basis of existing technology, through analysis and experiment, this paper adopts color road edge detection combined with road area recognition technology, which can achieve better results for unstructured road recognition in campus environment. The algorithm also has a certain recognition effect on the complicated road environment in the field. In this paper, the three-frame difference method combined with pyramid optical flow method is used to detect the moving object in the road and determine its position in the image. The main contents of this paper are divided into four aspects: mobile robot software system design, image road area recognition, road edge detection, obstacle detection. In the design of mobile robot software system, this paper mainly focuses on the design of vision navigation system of mobile robot. The function of the visual navigation system is to complete the navigation task by integrating the information flow of the scene obtained from the sensor through the integrated processing of the system modules such as road recognition, obstacle detection, motion decision-making and so on. The design of visual navigation system is accomplished by modularization and multithreading, so that the system has better maintainability and real-time. In the aspect of road area recognition, this paper reviews the common algorithm types of road recognition, and introduces the road recognition based on region in detail. In this paper, the color feature combined with LBP texture feature is used as the total image feature, and the supervised training method is adopted. The classifier is trained by using the SVM algorithm, which has a good effect of binary classification. The trained classifier is used to rough recognize the road area of the image. In the aspect of road edge detection, this paper analyzes the effect of five commonly used edge detection operators in road recognition, and adopts an improved Canny edge detection algorithm in color space combined with experimental analysis. The improved Canny edge detection is different from the traditional algorithm in calculating the mean difference between two directions in the neighborhood of 2x2. In this paper, we use the partial interpolation of 3x3 in four directions, at the same time, due to the continuous change of the mobile robot scene, In this paper, we do not use the fixed threshold of the traditional method, but adopt the adaptive threshold of the maximum inter-class variance according to each image. The improved Canny edge detection algorithm is more suitable for autonomous navigation of mobile robot. In the aspect of obstacle detection, this paper mainly studies the moving obstacle detection in the process of mobile robot navigation. In this paper, three common algorithms for obstacle detection are introduced in detail: background model difference method, two frame difference method and optical flow method. Then according to the hardware resources of the mobile robot, an obstacle detection algorithm based on three-frame difference and pyramid optical flow is used on the premise of satisfying the real-time and accuracy. It can detect the moving objects in front of the mobile robot in real time.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP242

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 武懷金;王武江;;基于幀間差分方法的運(yùn)動(dòng)目標(biāo)檢測(cè)[J];黑龍江科技信息;2012年10期

2 劉威;于紅緋;楊恒;段勃勃;袁淮;趙宏;;一種新的基于單目視覺的廣義障礙物檢測(cè)方法[J];電子學(xué)報(bào);2011年08期

3 薛麗霞;羅艷麗;王佐成;;基于幀間差分的自適應(yīng)運(yùn)動(dòng)目標(biāo)檢測(cè)方法[J];計(jì)算機(jī)應(yīng)用研究;2011年04期

4 楊s，

本文編號(hào)：2437071