基于RGB-D的室內(nèi)場景SLAM方法研究
發(fā)布時間：2018-08-08 14:34
【摘要】：近年來,隨著計算機(jī)技術(shù)的迅猛發(fā)展,同時定位與建圖(Simultaneous Localization and Mapping,SLAM)技術(shù)在移動機(jī)器人、無人機(jī)、無人駕駛、視覺醫(yī)療、AR/VR、可穿戴設(shè)備等方面得到了廣泛的應(yīng)用。隨著圖優(yōu)化問題中稀疏矩陣的發(fā)現(xiàn),基于視覺的SLAM方法已經(jīng)成為國內(nèi)外的研究熱點,基于圖優(yōu)化的SLAM方法逐漸應(yīng)用于在大規(guī)模場景中。本文采用華碩Xtion Pro Live深度相機(jī)作為傳感器,提出了一種基于改進(jìn)BoVW模型的三維SLAM方法,在本文提出的SLAM方法中,在圖像檢測和閉環(huán)檢測算法上提出了改進(jìn),并通過實驗證明提高SLAM的效率和魯棒性。首先,介紹了基于視覺SLAM的基本原理和方法。對SLAM問題進(jìn)行了描述,分析了幾種經(jīng)典的SLAM方法,對比了幾種經(jīng)典的特征檢測的優(yōu)缺點,針對視覺SLAM對圖像特征提取的要求,在ORB特征提取算法上提出了一種基于自適應(yīng)的區(qū)域分割ORB特征提取方法。并在圖像特征匹配方法上,采用傳統(tǒng)的隨機(jī)采樣一致性(Random Sample Consensus,RANSAC)算法和K近鄰(K-Nearest Neighbor algorithm,KNN)算法消除誤匹配,有效地減少誤匹配點數(shù),提高了匹配的精度和速度。在點云數(shù)據(jù)融合算法上,采用迭代最近(Iterative Closet Point,ICP)算法,用奇異分解(SVD)方法進(jìn)行求解計算相機(jī)位姿。其次,在閉環(huán)檢測方法上,介紹了閉環(huán)檢測的作用和方法,及閉環(huán)檢測中的問題和難點,在基于BoVW模型的閉環(huán)檢測方法中,介紹了視覺詞典的創(chuàng)建方法,相對于傳統(tǒng)K-Means聚類算法的缺點,提出了一種改進(jìn)的K-Means算法,有效地解決了K-Means算法依賴初始聚類中心,容易陷入局部最優(yōu)的問題,提高了閉環(huán)檢測的準(zhǔn)確率。最后,設(shè)計了一種基于RGB-D的室內(nèi)場景SLAM系統(tǒng),并通過實驗把本文改進(jìn)的算法應(yīng)用于該SLAM方法中。
[Abstract]:In recent years, with the rapid development of computer technology, simultaneous location and mapping (Simultaneous Localization and mapping slam) technology has been widely used in mobile robots, unmanned aerial vehicles, visual medical AR-VR, wearable devices and so on. With the discovery of sparse matrix in graph optimization problem, SLAM method based on vision has become a hot topic at home and abroad, and SLAM method based on graph optimization is gradually applied in large-scale scene. In this paper, using Asus Xtion Pro Live depth camera as sensor, a 3D SLAM method based on improved BoVW model is proposed. In the proposed SLAM method, the image detection and close-loop detection algorithms are improved. The experimental results show that the efficiency and robustness of SLAM are improved. Firstly, the basic principle and method of visual SLAM are introduced. This paper describes the SLAM problem, analyzes several classical SLAM methods, compares the advantages and disadvantages of several classical feature detection methods, and aims at the requirements of visual SLAM for image feature extraction. An adaptive region segmentation ORB feature extraction method based on ORB feature extraction algorithm is proposed. In the image feature matching method, the traditional random sampling consistent (Random Sample ConsensusRANSAC algorithm and the K-Nearest Neighbor algorithm KNN algorithm are used to eliminate the mismatch, which can effectively reduce the number of mismatch points and improve the accuracy and speed of the matching. In the point cloud data fusion algorithm, the iterative nearest (Iterative Closet Point ICP algorithm and singular decomposition (SVD) method are used to calculate the camera pose. Secondly, the function and method of closed-loop detection are introduced, and the problems and difficulties in closed-loop detection are introduced. In the close-loop detection method based on BoVW model, the method of creating visual dictionary is introduced. Compared with the traditional K-Means clustering algorithm, an improved K-Means algorithm is proposed, which effectively solves the problem that the K-Means algorithm depends on the initial clustering center and is prone to fall into the local optimal condition, and improves the accuracy of closed-loop detection. Finally, an indoor scene SLAM system based on RGB-D is designed, and the improved algorithm is applied to the SLAM method through experiments.
【學(xué)位授予單位】：湖南工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2172093
[Abstract]:In recent years, with the rapid development of computer technology, simultaneous location and mapping (Simultaneous Localization and mapping slam) technology has been widely used in mobile robots, unmanned aerial vehicles, visual medical AR-VR, wearable devices and so on. With the discovery of sparse matrix in graph optimization problem, SLAM method based on vision has become a hot topic at home and abroad, and SLAM method based on graph optimization is gradually applied in large-scale scene. In this paper, using Asus Xtion Pro Live depth camera as sensor, a 3D SLAM method based on improved BoVW model is proposed. In the proposed SLAM method, the image detection and close-loop detection algorithms are improved. The experimental results show that the efficiency and robustness of SLAM are improved. Firstly, the basic principle and method of visual SLAM are introduced. This paper describes the SLAM problem, analyzes several classical SLAM methods, compares the advantages and disadvantages of several classical feature detection methods, and aims at the requirements of visual SLAM for image feature extraction. An adaptive region segmentation ORB feature extraction method based on ORB feature extraction algorithm is proposed. In the image feature matching method, the traditional random sampling consistent (Random Sample ConsensusRANSAC algorithm and the K-Nearest Neighbor algorithm KNN algorithm are used to eliminate the mismatch, which can effectively reduce the number of mismatch points and improve the accuracy and speed of the matching. In the point cloud data fusion algorithm, the iterative nearest (Iterative Closet Point ICP algorithm and singular decomposition (SVD) method are used to calculate the camera pose. Secondly, the function and method of closed-loop detection are introduced, and the problems and difficulties in closed-loop detection are introduced. In the close-loop detection method based on BoVW model, the method of creating visual dictionary is introduced. Compared with the traditional K-Means clustering algorithm, an improved K-Means algorithm is proposed, which effectively solves the problem that the K-Means algorithm depends on the initial clustering center and is prone to fall into the local optimal condition, and improves the accuracy of closed-loop detection. Finally, an indoor scene SLAM system based on RGB-D is designed, and the improved algorithm is applied to the SLAM method through experiments.
【學(xué)位授予單位】：湖南工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
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