本文選題：雙目立體視覺 + 同時定位與地圖構建�。� 參考：《東華大學》2017年碩士論文

【摘要】：同步定位與地圖構建(Simultaneous location and mapping,SLAM)是指機器人在未知環(huán)境中創(chuàng)建環(huán)境地圖并推斷自身位姿的過程。近年來,SLAM問題的解決對于移動機器人實現(xiàn)自主定位具有十分重要的意義,已逐漸成為移動機器人導航及計算機視覺領域的熱門研究課題。由于SLAM中機器人的特征點匹配往往出現(xiàn)誤匹配且匹配的復雜度較高而導致機器人構建地圖的周期較長,定位的實時性較差。為此,我們對SLAM的特征匹配與定位技術展開理論研究。本文針對雙目立體視覺SLAM系統(tǒng)展開研究,具體工作如下:(1)介紹了SLAM系統(tǒng)框架,分析了兩種常用的SLAM濾波算法—擴展卡爾曼濾波器算法和粒子濾波器算法。給出了本文研究雙目立體視覺SLAM的觀測與運動兩大基本模型,在此基礎上提出了本文研究的雙目立體視覺SLAM的整體結構框架。(2)對雙目立體視覺SLAM的數(shù)據(jù)關聯(lián)問題進行了研究。介紹了特征提取與匹配技術的相關知識,并針對SIFT特征提取與匹配算法的維數(shù)較大且存在較大的誤匹配率問題,結合支持向量機(SVM)的序列最小優(yōu)化算法(SMO)進一步細匹配提出基于序列最小優(yōu)化的SIFT特征提取與匹配算法—SMO-SIFT算法。最后通過MATLAB仿真表明SMO-SIFT算法降低了算法的維數(shù),改善了算法的實時性,同時提高了算法精確度。(3)對SLAM的路徑估計問題進行了研究。介紹了RaoBlackwellised粒子濾波器算法(RBPF)并針對RBPF算法的粒子數(shù)目的增加會頻繁重采樣從而導致“粒子退化”問題,提出了基于小生境遺傳優(yōu)化算法的INGO-RBPF算法。最后通過MATLAB仿真表明INGO-RBPF算法具較高的估計精度和穩(wěn)定性,抗干擾能力較強,比較適合應用在SLAM實時定位中。(4)在機器人操作系統(tǒng)(ROS)的環(huán)境下將SMO-SIFT和INGORBPF算法運用于實驗環(huán)境中。給出了SLAM系統(tǒng)中的地圖構建、機器人控制及遠程控制三大模塊的軟硬件設計,實驗結果表明機器人能夠正確的構建出環(huán)境地圖和成功定位,運行結果比較理想。
[Abstract]:Synchronous location and mapping (slam) is a process in which robots create environmental maps and infer their posture in unknown environments. In recent years, the solution of slam problem is very important for mobile robot to achieve autonomous positioning, and has gradually become a hot research topic in the field of mobile robot navigation and computer vision. Due to the false matching of robot feature points in slam and the high complexity of matching, the robot has a long period of map construction and poor real-time localization. Therefore, the feature matching and localization techniques of slam are studied theoretically. The main work of this paper is as follows: (1) the framework of slam system is introduced, and two commonly used slam filtering algorithms, extended Kalman filter algorithm and particle filter algorithm, are analyzed. In this paper, two basic models of observation and motion of binocular stereo slam are presented. Based on these two models, the whole frame of binocular stereo slam is proposed. (2) the data association problem of binocular stereo slam is studied. This paper introduces the knowledge of feature extraction and matching, and aims at the problem that sift feature extraction and matching algorithm has large dimension and large mismatch rate. Combined with support vector machine (SVM) sequence minimum optimization algorithm (SMO), a SMO-SIFT feature extraction and matching algorithm based on sequential minimum optimization is proposed. Finally, MATLAB simulation shows that SMO-SIFT algorithm reduces the dimension of the algorithm, improves the real-time performance of the algorithm, and improves the accuracy of the algorithm. (3) the path estimation problem of slam is studied. In this paper, Rao Blackwellised particle filter algorithm (RBPF) is introduced. Aiming at the problem of "particle degradation" caused by increasing the number of particles in RBPF algorithm, an INGO-RBPF algorithm based on niche genetic optimization algorithm is proposed. Finally, MATLAB simulation shows that INGO-RBPF algorithm has high estimation accuracy and stability, strong anti-jamming ability, and is more suitable for real-time localization of slam. (4) SMO-SIFT and INGORBPF algorithms are applied in the environment of robot operating system (Ros). The software and hardware design of the three modules in slam system are given. The experimental results show that the robot can correctly construct the map of environment and locate successfully, and the result of running is ideal.
【學位授予單位】：東華大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP242;TN713

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本文編號：2070737