本研究宗旨在使用室內空間特徵影像做為辨識標籤並結合建築資訊模型(Building Inform-ation Modeling)內所包含的空間資訊，目的是為了提供一個經濟的室內空間定位技術，使用戶可以透過智慧型手持裝置相機鏡頭擷取所在位置的空間特徵影像，得知目前所在位置及該空間資訊。
其方法和目前無線頻寬(Wireless)和無線射頻(Radio Frequency IDentification)室內定位技術有所不同，本研究應用的室內空間特徵影像做為位置識別標籤技術。其架構包括三個功能模組，（1）空間特徵影像資料庫，（2）空間特徵影像管理模組，（3）空間特徵影像辨識定位模組。空間特徵影像資料庫是從建築資訊模型(Building Information Modeling)收集來自工業基礎類別標準(Industry Fo-undation Classes)相關空間資訊及相對應空間的特徵影像。空間特徵影像管理模組為拍攝空間特徵影像，與資料庫連結並建立空間特徵影像集。空間特徵影像辨識定位模組是使用擴增實境技術，運用D'fusion軟體的影像辨識技術，透過手持裝置擷取所在空間特徵影像，進行影像辨識定位後，連結到資料庫將所在空間資訊顯示在手持裝置上。
結果與討論是以BIM與影像辨識為基礎之空間辨識雛型系統，透過智慧型手持裝置的攝影鏡頭在Android平台上運行，如智慧型手機和平板電腦。透過不斷的技術測試，以了本研究的技術的可行性。根據多次測試結果，本研究室內定位雛型系統可以識別空間位置及使用者所在的空間資訊，然而當室內空間是缺乏特徵影像而無法辨識時，如室內空間為單調的牆壁及相似度過高，未能被識別。為了克服這種情況，將以二維空間標籤的QR碼(Quick Response Code)，替代空間特徵影像作為辨識標籤。此外，由於空間資訊是從現有的建築資訊模型而來的，可以保證資訊的一致性。之後並透過經濟可行性進行分析評估其成本與效益。

Purpose  In order to provide an economical indoor location detective technique, this study is aimed to use photo images as the indoor spatial identification tags associated with the spatial information of the existing building information model (BIM), so that users can identify their locations via the camera on mobile device based on the real images.  Method  Unlike the wireless and RFID-based indoor positioning techniques, this study applied the image recognition technique to indoor location detection. Prototype architecture includes three functional modules, namely, (1) Spatial image database, (2) spatial image management module, and (3) vision-based localization module are developed. The spatial image database is the data collection of space related data from the IFC (Industry Foundation Classes) dataset of the existing BIM and space feature image data collected form users. The spatial image management module provides users an interface to collect the spatial photo images of buildings, and bind them with the location data from the spatial image database. Then, the vision-based localization module developed based on the D’fusion studio can identify the space location by recognizing the images from the vision captured with the camera on a smart phone, and the corresponding spatial data can be retrieved from the database.  Results & Discussion The BIM-Vision-Based indoor localization prototype was developed as an android platform application running on the mobile device with imbedded camera such as smart phones and tablets. Technique feasibility is continuously tested in current phase. According to the basic test results, the prototype can identify the indoor locations of decorated spaces; however, once the indoor spaces are lack of recognition features, such as the empty spaces with blank and monotony walls, the recognition function failed. To overcome this defect, the Quick Response (QR) code, the trademark for a type of two-dimensional code, is used as a substitution of the photos for this prototype. Besides, since the location data is transferred from the existing building information model, the data consistency can be ensured. In the future, the economic feasibility of this prototype would be analyzed to evaluate the cost and benefit ration.

目錄
中文摘要	1
英文摘要	3
目錄 	5
圖目錄	7
表目錄	9
第1章 緒論	10
1.1研究動機	10
1.2研究目的	11
1.3研究方法及步驟	12
1.4研究範圍與限制	15
第2章 文獻回顧	16
2.1建築資訊模型（Building Information Modeling－BIM）	16
2.1.1工業基礎類別(Industry Foundation Classes－IFC)	17
2.2行動定位服務 (Location based Service－LBS)	20
2.2.1室內定位技術(Indoor Positioning System－IPS )	22
2.3擴增實境技術(Augmented Reality－AR)	27
2.3.1擴增實境影像辨識技術	29
2.3.2現有的開發軟體	32
第3章系 雛形系統架構之建立	37
3.1系統分析	37
         3.1.1情境案例	37
         3.1.2流程模型建立	38
         3.1.3情境之建立	40
         3.1.4繪製系統USE CASE圖	41
3.2系統設計	44
3.2.1建立系統架構及開發環境	44
3.2.2空間特徵影像資料庫設計	46
3.2.3空間分類	49
3.2.4空間特徵影像拍攝模組	52
3.2.5空間特徵影像辨識定位模組	54
第4章系 系統實作與展示	56
4.1空間特徵影像空料庫	56
4.2空間特徵影像管理模組	57
4.3空間特徵影像辨識定位模組	62
第5章 系統測試與分析	66
5.1系統測試	66
5.2經濟性分析	67
第6章 結論與建議	72
6.1研究結論	72
6.2未來建議	73
參考文獻	74

圖目錄
圖1.1研究流程	12
圖2.1 IFC 整體架構	19
圖2.2 RFID 產品分類	25
圖2.3 RFID TAG 性質	26
圖2.4頭戴顯示器	27
圖2.5真實-虛擬連續性	28
圖2.6標記標籤	29
圖2.7 QR-CODE標籤	30
圖2.8 QR CODE結合擴增實境之應用	31
圖3.1特徵影像拍攝流程模型	38
圖3.2特徵影像辨識定位流程模型	39
圖3.3使用案例圖－空間特徵影像管理模組	42
圖3.4使用案例圖－空間特徵影像辨識定位模組	42
圖3.5雛型系統功能模組規劃	43
圖3.6雛型系統架構圖	45
圖3.7系統整合示意圖(CLIENT/SERVER)	46
圖3.8空間特徵影像資料庫ER MODEL	48
圖3.9 IFC空間標籤	49
圖3.10室內空間分類圖	51
圖3.11特徵影像拍攝機制	53
圖3.12特徵影像集與空間名稱配對	54
圖3.13空間特徵影像辨識定位演算法	55
圖4.1 IFC匯入流程	56
圖4.2系統主畫面	57
圖4.3空間特徵影像管理模組執行程序	58
圖4.4特徵影像拍攝示意圖	58
圖4.5特徵影像管理選單	59
圖4.6空間名稱清單	59
圖4.7選擇空間名稱	60
圖4.8選擇空間名稱完成	60
圖4.9拍攝第一張特徵影像	61
圖4.10拍攝第二張特徵影像	61
圖4.11拍攝第三張特徵影像	61
圖4.12上傳至資料庫	62
圖4.13空間特徵影像辨識定位模組執行程序	63
圖4.14空間特徵影像辨識定位示意圖	63
圖4.15開始辨識空間特徵影像	64
圖4.16辨識出空間特徵影像1	64
圖4.17辨識出空間特徵影像2	65
圖4.18辨識出空間特徵影像3	65
圖4.19顯示其空間資訊	65
圖5.1各技術成本數量價格匯整	69
圖5.2各技術總成本匯整	69
圖5.3 室內定位技術總成本分析	70

表目錄
表1.1 D'FUSION硬體需求表	15
表3.1特徵影像拍攝情境	40
表3.2特徵影像辨識定位情境	41
表3.3各空間特徵影像擷取及拍攝位置	51
表5.1空間辨識度測試彙整表	66

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