本文選題：移動機器人 + 三維地圖構(gòu)建�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著移動機器人在工業(yè)制造、未知環(huán)境探索、人類服務(wù)、軍事等領(lǐng)域的廣泛應(yīng)用,研究如何實現(xiàn)移動機器人的自主定位與導(dǎo)航一直是機器人領(lǐng)域的熱點問題。1988年提出的SLAM(Simultaneous Localization and Mapping,即時定位與構(gòu)圖)技術(shù)被眾多研究人員認為是實現(xiàn)移動機器人自主化的關(guān)鍵。而在SLAM技術(shù)中地圖構(gòu)建作為其中重要的組成部分,其精度和完整性是SLAM技術(shù)得以實現(xiàn)的基礎(chǔ)。目前,在移動機器人地圖構(gòu)建理論和方法的研究中,三維地圖相對于二維地圖實用價值更高,應(yīng)用更加廣泛,且研究成果少,對于研究出環(huán)境適應(yīng)性強,高效實用的三維地圖構(gòu)建方法有著迫切的需要。本文的研究目的是針對移動機器人三維地圖構(gòu)建中的相關(guān)問題,對移動機器人三維地圖構(gòu)建中的關(guān)鍵技術(shù)展開研究,并在室內(nèi)環(huán)境下驗證相關(guān)算法的可行性。圍繞這一目的,主要進行了以下的研究工作。設(shè)計并搭建了基于雙目視覺的移動機器人三維地圖構(gòu)建平臺。本文深入分析了移動機器人運行環(huán)境特點,采用系統(tǒng)集成的方法完成室內(nèi)環(huán)境下移動機器人三維地圖構(gòu)建平臺的設(shè)計與調(diào)試。在充分了解平臺的運動特性和機械結(jié)構(gòu)基礎(chǔ)上,建立了機器人平臺運動模型,并采用航位推算方法實現(xiàn)了機器人相對位姿估計。研究基于立體視覺的三維點云數(shù)據(jù)獲取方法。文中在對攝像機幾何光學(xué)模型和畸變模型進行分析的基礎(chǔ)上,對雙目視覺中涉及的立體匹配等算法展開研究。著重探討了影響立體匹配效果的因素,并結(jié)合三維地圖構(gòu)建的實際要求,通過邊緣檢測的方法獲取環(huán)境中關(guān)鍵邊緣特征,并通過雙目測距原理完成空間中單幀三維關(guān)鍵特征點云數(shù)據(jù)獲取。研究點云預(yù)處理和三維點云配準方法。文中針對室內(nèi)環(huán)境下的粗糙地面特征及光滑地面反射特征問題,提出了基于最小二乘法的地面特征去除方法,并結(jié)合點云過濾器和統(tǒng)計分析方法完成三維點云配準前的預(yù)處理。根據(jù)點云分布特征及位姿估計精度,選擇正態(tài)分布算法完成連續(xù)多幀點云圖的拼接及配準,并提出了基于邊緣特征的三維點云配準方法。本文比較了邊緣關(guān)鍵特征點云和普通稀疏點云在點云配準中的實際效果,驗證了本文提出的點云配準方法的有效性。為了驗證本文所研究的三維地圖構(gòu)建方法的可行性,文中選擇典型的室內(nèi)樓道環(huán)境,控制系統(tǒng)中的移動機器人平臺,完成室內(nèi)環(huán)境下全局三維地圖的創(chuàng)建,并對三維地圖構(gòu)建結(jié)果進行了效果分析和關(guān)鍵影響因素分析。實驗結(jié)果表明本文所構(gòu)建的移動機器人三維地圖能夠滿足移動機器人自主定位和導(dǎo)航的要求。
[Abstract]:With the wide application of mobile robots in industrial manufacturing, unknown environment exploration, human service, military and other fields, The research on how to realize autonomous localization and navigation of mobile robots has always been a hot issue in the field of robot. In 1988, the technology of slam localization and mapping was considered by many researchers to be the key to realize autonomous mobile robot. Map construction is an important part of slam technology, and its precision and integrity are the foundation of slam technology. At present, in the research of mobile robot map construction theory and method, 3D map has higher practical value, wider application and less research results compared with two-dimensional map, so it has strong adaptability to research environment. Efficient and practical three-dimensional map construction method has an urgent need. The purpose of this paper is to study the key technology of 3D map construction of mobile robot and verify the feasibility of the algorithm in indoor environment. Around this purpose, mainly carried out the following research work. A three-dimensional map building platform for mobile robot based on binocular vision is designed and built. In this paper, the characteristics of mobile robot running environment are deeply analyzed, and the design and debugging of mobile robot 3D map construction platform in indoor environment are completed by system integration method. On the basis of fully understanding the motion characteristics and mechanical structure of the platform, the motion model of the robot platform is established, and the relative position and attitude estimation of the robot is realized by using the method of dead-reckoning. Three-dimensional point cloud data acquisition method based on stereo vision is studied. Based on the analysis of geometric optical model and distortion model of camera, the stereo matching algorithms involved in binocular vision are studied in this paper. This paper mainly discusses the factors that affect the effect of stereo matching, and combines with the actual requirements of 3D map construction, obtains the key edge features of the environment by edge detection. The key feature cloud data in a single frame is obtained by binocular ranging principle. Point cloud preprocessing and three-dimensional point cloud registration are studied. In order to solve the problem of rough ground feature and smooth ground reflection feature in indoor environment, a method of ground feature removal based on least square method is proposed, and the pre-processing of 3D point cloud registration is completed by combining point cloud filter and statistical analysis method. According to the distribution characteristics of point clouds and the accuracy of position and pose estimation, the normal distribution algorithm is selected to complete the stitching and registration of continuous multi-frame point cloud images, and a 3D point cloud registration method based on edge features is proposed. In this paper, the effectiveness of the point cloud registration method proposed in this paper is verified by comparing the effect of point cloud registration with that of common sparse point cloud. In order to verify the feasibility of the 3D map construction method studied in this paper, the paper selects the typical indoor corridor environment and the mobile robot platform in the control system to complete the creation of the global 3D map in the indoor environment. The result of 3D map construction is analyzed and the key factors are analyzed. The experimental results show that the 3D map of mobile robot can meet the requirements of autonomous localization and navigation of mobile robot.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP242

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