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系統識別號 U0026-1208202010351100
論文名稱(中文) 基於慣導整合之室內導航底圖快速建置技術與類神經輔助之影像室內定位
論文名稱(英文) The Development of an Image Enhanced Indoor Positioning System Utilizing Cascade Correlation Neural Networks and On-site Generated Indoor Navigation Maps
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 108
學期 2
出版年 109
研究生(中文) 洪渼芹
研究生(英文) Mei-Chin Hung
學號 P66074036
學位類別 碩士
語文別 英文
論文頁數 97頁
口試委員 指導教授-江凱偉
口試委員-卓大靖
口試委員-黃智遠
中文關鍵字 室內製圖  移動測繪系統  影像室內定位  階層關聯式神經網路  行人航位推算 
英文關鍵字 Indoor mapping  Mobile Mapping System (MMS)  Image-based indoor positioning  Cascade Correlation neural Network (CCN)  Pedestrian Dead Reckoning (PDR) 
學科別分類
中文摘要 近年來,隨著物聯網產業的發展,適地性服務(Location-Based Service, LBS)被廣泛應用在各層面。提供相對應使用者或目標物的位置在實踐適地性服務上是絕對且必要的,一般而言,身處透空度良好的地區,全球導航衛星系統(Global Navigation Satellite System, GNSS)在定位上扮演重要角色;相反的,當使用者位在衛星訊號遮蔽區,建構室內定位系統則需高度仰賴其他定位技術。其中,基於影像發展的室內定位系統具有低成本、高效率且無須外部設施…等符合現今使用者需求之優點,故本研究提出基於自行設計標籤進行影像室內定位。為清楚展示使用者所在位置,室內製圖系統需於先期建置導航用底圖,然而傳統上進行數化並採用導線測量以量取控制點之絕對座標不僅過於耗時,人力成本上也是考量的一大重點,故本研究提出一影像處理方法自動萃取出利用相機所拍攝的逃生用圖之特徵,並使用車載移動式製圖系統獲取空間中控制點座標,不僅大幅降低時間與人力的耗費,座標轉換之精度也能滿足後續定位之相關應用,視覺化的呈現協助使用者了解自身的位置資訊。
至於影像室內定位系統則延伸衛星定位之概念,利用自行設計之標籤取代衛星,一旦成功由影像辨識演算法偵測並辨識到標籤,相對應的座標即可從預先建置的資料庫中取得;衛星定位中的虛擬距離則使用階層關聯式神經網路 (Cascade Correlation neural Network, CCN) 學習標籤於影像中的變形量及其他相關資訊後即可推估,同時具備三個標籤之物空間座標與相對應距離,利用三邊交會解算出使用者所在位置。本研究中所提出的影像辨識演算法其辨識率達97%,平均耗時約11秒,此外利用神經網路推估的距離精度約為0.2公尺,整體定位精度落在1-2.5公尺左右,符合室內行人定位之精度需求。最後整合影像室內定位技術與行人航位推算以模擬行人對於該室內定位系統之使用,利用本研究所提出的影像室內定位技術提供初始位置與更新位置之座標,以降低行人航位推算隨時間累積的誤差,可明顯地提升定位精度,整體閉合差之提升率為62%。
英文摘要 With the development of the Internet of Things (IoT), Location-Based Service (LBS) becomes a popular issue that is extensively applied in recent years. To put LBS into practice, positioning is an essential technique. Relying on Global Navigation Satellite System (GNSS) is an appropriate strategy in an open sky area. In contrast, in a GNSS-hostile environment, others positioning techniques are in demand practically. Therefore, image-based indoor positioning technology, with the advantages of low cost, high efficiency, and no need for external devices, is a desirable technology for realizing the indoor positioning.
Before starting the positioning, generating a floor plan for revealing the user’s position is important as well. Comparing with the conventional method for mapping, the proposed rapid mapping method, which automatically extracts the features of the photographed evacuation plan through image processing and acquiring coordinates of the control points by Mobile Mapping System (MMS) for the transformation is efficient. Not only decrease the operation time significantly but also reduce the personnel cost. In addition, the accuracy of the transformation by the proposed method, the RMSE can achieve approximately 1 meter.
As for the proposed indoor positioning method, the concept of the algorithm of GNSS, trilateration, is adapted. Above all, the detection algorithm is used for searching for a self-designed marker, as soon as the marker recognized, the corresponding coordinate is obtained from the pre-constructed database. The recognition rate can achieve 97%, moreover, the average of the consuming time only 11 seconds. Next, this research proposed a novel method of estimating the distance between the marker and the camera by the deformation of the marker within the image through Cascade Correlation neural Network (CCN). The RMSE of the distance estimation is 0.2 meters. At least three coordinates of the respective markers and the distances acquired simultaneously, the position of the user can be calculated by trilateration. The overall accuracy falls in the range around 1-2.5 meters, which is sufficient for indoor pedestrian navigation. Hence, PDR integrates with image-based indoor positioning is executed in the end. The positions of the starting point and the updating are provided by the proposed image-based method. The result suggests that the entire improvement of the complete proposed indoor positioning system can achieve up to 62%.
論文目次 中文摘要 I
Abstract II
Acknowledgements IV
Contents V
List of Tables VIII
List of Figures IX
Chapter 1 Introduction 1
1.1 Literature Review 1
1.2 Motivation and Objectives 4
1.3 Thesis Outline 5
Chapter 2 Research Background 7
2.1 Coordinate System and Transformations 7
2.1.1 Conventional Terrestrial System (CTS) 7
2.1.2 North-east-down (NED) coordinate system 9
2.1.3 Image coordinate system 10
2.1.4 Pixel coordinate system 13
2.2 Surveying and mapping 14
2.2.1 Surveying 14
2.2.2 Indoor Navigation Map Generation Technologies 16
2.3 Indoor positioning technologies 20
2.4 Artificial Neural Networks 26
2.4.1 The elements of neuronal model 26
2.4.2 ANNs architectures 28
2.4.3 Learning processes 30
2.4.4 Cascade Correlation neural Networks 31
Chapter 3 Methodology 38
3.1 On-site Navigation Map Generation techniques 38
3.1.1 Map generation 38
3.1.2 Acquiring the coordinates of control points 41
3.1.3 Coordinate transformation 46
3.2 Marker-based positioning 49
3.2.1 The self-designed marker 49
3.2.2 Detection and recognition of the marker 50
3.3 Distance estimation and trilateration 57
3.3.1 Distance estimation by CCN 57
3.3.2 Trilateration 59
3.4 Pedestrian Dead Reckoning 61
Chapter 4 Experiments and Analysis 65
4.1 Experimental Settings and Scenarios Descriptions 65
4.1.1 Description of Devices 65
4.1.2 Description of Experiment 69
4.2 The Performance of the Rapid Map Generation Method 70
4.3 The Accuracy of Image-based Indoor Positioning Method 78
4.3.1 The accuracy of trained CCN model 78
4.3.2 The result of positioning method 82
4.3.3 The performance of PDR integrated with proposed method 86
Chapter 5 Conclusions and Future Works 90
5.1 Conclusions 90
5.2 Future Works 91
References 93

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