本文選題：移動增強現(xiàn)實　切入點：大范圍場景　出處：《華中科技大學》2013年博士論文　論文類型：學位論文

【摘要】：隨著智能手機的廣泛使用，移動AR技術更加受到國內外研究人員的關注。由于諸如智能手機之類的移動終端設備相對于PC機具有資源受限的特點，比如，計算速度慢、內存空間有限、手機功耗問題等，同時，智能手機輕便、體積小、可隨身攜帶等特點，又擴大了使用者的活動范圍，因此，需要在智能手機上提供能夠完成大范圍場景定位識別和三維注冊的移動AR技術，但是，又不能簡單的把應用于PC機上的增強現(xiàn)實技術移植到智能手機上。基于以上內容，本文提出了可以直接在移動設備上實現(xiàn)大范圍場景的定位識別和三維注冊的系統(tǒng)構架，主要研究工作如下： 第一，通過使用重力來增強局部向量聚集描述符VLAD的鑒別力，設計了GAVLAD圖像描述符，并設計了一個有效的向量量化策略，能將高維圖像描述符壓縮成幾個字節(jié)的壓縮編碼，將圖像描述符編碼成幾個字節(jié)，這樣可以存儲在移動設備中，有助于完成高效搜索，并以此設計了一個適合移動設備RAM的圖像搜索引擎，使其能夠高效完成移動設備上的定位識別。本文還建構了一個基于圖像和傳感器的相結合的壓縮的索引結構，能在移動設備上直接處理大范圍圖像數(shù)據(jù)。 第二，設計了一個簡單有效的向量二值化方法以減少多特征融合內存占用量，并提出了一個位置敏感的融合算法將多特征融合起來。該算法可以將多個圖像特征壓縮成一個占用空間小的、高區(qū)分度的圖像描述符，可以直接在移動設備上高效的進行存儲和搜索。并提出將特征融合與索引結構聯(lián)合優(yōu)化，以提高定位識別的準確性，同時減少內存占用。 第三，設計了一個靈活的攝像機初始化和追蹤方法，可以以高達10Hz每幀的幀率在現(xiàn)階段主流配置的手機上追蹤非平面場景，一定程度上解決了大范圍場景移動增強現(xiàn)實應用中的虛實注冊問題。 第四，發(fā)布了一套新的數(shù)據(jù)庫，包含1,295,000個地理標記街景圖像以及849個測試查詢圖像，這些資源可以被用作新的參照基準，可以在今后的相關研究中作為參照基準供其他研究人員繼續(xù)使用。 本文通過多組實驗證明，，本研究提出的基于移動設備的大范圍場景定位識別和三維注冊移動AR系統(tǒng)提高了定位識別的精確性，并在節(jié)省內存、提高速度等方面取得了滿意的效果，為提高移動增強現(xiàn)實系統(tǒng)的真實感和促進其走出實驗室、面向廣泛應用提供必要的技術支撐。
[Abstract]:With the wide use of smart phones, mobile AR technology has attracted more attention from researchers at home and abroad. Because mobile terminal devices such as smart phones have the characteristics of limited resources compared with PCs, for example, the computing speed is slow. Limited memory space, mobile phone power problems, and so on, at the same time, smart phones are light, small, portable and other characteristics, but also expand the range of activities of users, so, We need to provide mobile AR technology on the smartphone that can complete the large-scale scene location recognition and 3D registration, but we can't simply transplant the augmented reality technology applied to the PC to the smart phone. This paper presents a system framework that can directly realize the location recognition and 3D registration of large-scale scene on mobile devices. The main research work is as follows:. First, by using gravity to enhance the discriminant ability of the local vector aggregation descriptor (VLAD), the GAVLAD image descriptor is designed, and an effective vector quantization strategy is designed, which can compress the high-dimensional image descriptor into several bytes of compression coding. The image descriptor is encoded into several bytes, which can be stored on a mobile device, which is helpful for efficient search, and an image search engine suitable for the mobile device RAM is designed. This paper also constructs a compressed index structure based on image and sensor, which can directly process large range image data on mobile devices. Secondly, a simple and effective vector binarization method is designed to reduce the memory footprint of multi-feature fusion. A position sensitive fusion algorithm is proposed, which can compress multiple image features into a small space and high partition image descriptor. It can be stored and searched directly on mobile devices, and it is proposed to optimize the feature fusion and index structure to improve the accuracy of location identification and reduce the memory footprint. Thirdly, a flexible camera initialization and tracking method is designed, which can track non-planar scenes at a frame rate of up to 10 Hz per frame on the current mainstream mobile phone. To some extent, the problem of virtual reality registration in large scale mobile augmented reality applications is solved. In 4th, a new database containing 1, 295,000 geo-marked streetscape images and 849 test query images was released, which can be used as a new reference frame. It can be used as a reference for other researchers in future studies. In this paper, it is proved by many experiments that the large scale scene location recognition based on mobile device and 3D registered mobile AR system can improve the accuracy of location recognition and save memory. In order to improve the reality of mobile augmented reality system and promote it out of the laboratory, it provides the necessary technical support for the wide application of mobile augmented reality system.
【學位授予單位】：華中科技大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：TP391.41

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