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系統識別號 U0026-1701201815510300
論文名稱(中文) 基於智慧型手機發展室內行人定位整合系統的多種優化策略
論文名稱(英文) Using Smartphones for Enhanced Integrated System Strategies for Indoor Pedestrian Positioning
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 106
學期 1
出版年 106
研究生(中文) 廖振凱
研究生(英文) Jhen-Kai Liao
電子信箱 cacalut1690@gmail.com
學號 P68011050
學位類別 博士
語文別 英文
論文頁數 359頁
口試委員 口試委員-曾義星
口試委員-卓大靖
口試委員-韓仁毓
口試委員-陳國華
口試委員-賴盈誌
指導教授-江凱偉
共同指導教授-張秀雯
中文關鍵字 室內定位及導航  智慧型手機  人工智慧  移動製圖  感測器率定 
英文關鍵字 indoor positioning and navigation  smartphone  artificial intelligence  mobile mapping  sensor calibration 
學科別分類
中文摘要 近年來,智慧型手機已成為日常生活中相當普遍的裝置,同時由於微機電感測元件的性能提升與成本降低,使得智慧型手機大多內建有各式感測器,以滿足基本的使用者需求,並由此開始延伸並發展各種不同的行動應用。適地性服務(Location-based Services, LBS)與物聯網(Internet of Things, IoT)受到智慧型手機普及與微感測元件成本降低的影響,是目前頗受關注的熱門議題,被視為是人類科技生活下一個世代的演進方向。構成LBS或IoT應用的核心元素之一即是裝置或使用者的位置資訊,而由此衍生的需求讓具有20年發展史的定位技術再度獲得重視,尤其在行動定位與室內定位的部分變得相當熱門。儘管全球導航衛星系統(Global Navigation Satellite System, GNSS)的精度與穩定性已相當成熟,使得室外的位置資訊擁有非常可靠的來源,但進入室內空間,衛星訊號受到嚴重遮蔽,這時如何獲取精準的位置資訊就成為一個挑戰。

室內定位技術在過去已經有許多研究發展出成熟的系統或演算法,但由於不同的時空背景,過去的專家多提倡裝設額外的布建設施與穿戴裝置,這並不符合現今基於智慧型手機為主,並盡量減低使用者成本的發展潮流。此外,其他技術也各自面臨難以推廣的阻礙,例如需要模型的先驗知識或事後調校,而缺乏即時性;需要額外的使用者裝置或環境設備,使得成本增加;需要連續拍攝影像或專家知識的操作程序,對使用者不夠友善;需要時間成本建置繁雜的環境資料庫,卻缺乏對抗環境變化的能力;定位所需的計算量龐大,要求較高的硬體效能;受限智慧型手機感測元件規格,誤差會快速累積等等。簡言之,符合目前人類生活與產業發展的趨勢,且具備絕對優勢的室內定位技術尚未出現,肇因於不同類型之定位技術先天所擁有的缺點。故室內定位技術雖然不是新興的領域,但近年仍吸引專家學者前仆後繼地投入研究,期望能夠克服以上的挑戰,使室內定位技術能夠被廣泛應用在人類的生活。

如前所述,單一的定位系統通常是一種雙面刃,當系統使用其依據的觀測量與定位原理時,往往就不可避免的產生附帶影響。因此,整合與客製化的系統是現階段可行的方向之一:透過整合不同的室內定位技術,互補彼此的優缺點,並依據應用環境的條件選擇適用的技術客製化,才能達成既精準又便利的低成本室內定位服務。另一方面,空間資訊與測繪領域提供一種與資訊科學和電機工程不同的切入角度:坐標系統、準確的地圖與為了測繪需求所衍生的定位技術,是本領域在室內定位的核心優勢。故本研究基於前述的核心價值,針對數種定位技術發展優化演算法,並在不同的場景中,試驗由不同的定位技術所整合而成的演算法,並評估其定位精度。

本研究首先提出一個室內定位整合架構,以智慧型手機的感測元件為基礎,慣性定位技術為演算法核心,兼容不同的室內定位與情境辨識技術,期望能自動偵測並客製化以達成最佳的定位效能。為了驗證此一架構的可行性,本研究依據此一架構之各組成部位發展的演算法包含:1)走速約制/即時平滑演算法應用於室內行人的慣性積分導航;2)基於模糊決策樹的圖資輔助行人航位推算;3)影像空間後方交會輔助的行人航位推算,並進一步演進為基於類神經網路的影像定位輔助行人航位推算;4)基於差分定位原理發展的藍牙定位技術。簡言之,本研究提出數種室內定位技術的優化與整合方案,並初步驗證了對應的定位精度,說明本研究提出的室內定位整合架構具備極高的可行性,但因架構龐大且牽涉技術甚廣,架構的完整實現仍需待未來剩餘的組成部分完成研發,一旦整合完成,此一系統預計將能夠滿足室內定位與實務應用的需求。此外,了解智慧型手機感測元件的性能與誤差表現,攸關本研究的演算法參數與發展方向,同時圖資在本研究中亦佔有相當份量,因此感測器的率定方法與圖資測繪程序一併於附錄中討論。並提供室內定位系統建置經驗的分享,期望本論文除了在技術研究上有所貢獻以外,也可以成為一本室內定位與感測技術深入淺出的導讀文獻。
英文摘要 In recent years, smartphones have become nearly ubiquitous. Due to the increased accuracy and decreased cost of Micro-electro mechanical system (MEMS) sensors, the smartphone has come to be embedded with various sensors which improve its usefulness for various mobile applications. These have led to excitement over the future possibilities of Location-Based Services (LBS) and the Internet of Things (IoT). The core component of LBS and IoT is the position of the user or device, causing renewed attention to be given to twenty-year old positioning technologies related to mobile and indoor positioning. The accuracy and stability of Global Navigation Satellite Systems (GNSS) for outdoor positioning is already reliable and mature. However, indoors the blocked of satellite signals make it difficult to obtain accurate and continuous positioning.

Many kinds of mature indoor positioning technologies have been proposed in the past, but due to the different background of the past, most of these require additional infrastructure and wearable sensors rather than exploiting the latent usefulness of smartphones with their built-in low-cost sensors. It is true that this technology has limitations, such as the lack of real-time ability because of the need of prior knowledge and posterior modeling; the costs of additional infrastructure and devices; the tiresome need for successively taking images or applying expert knowledge; the time-consuming creation of an environmental database (which can suddenly become inaccurate due to environmental changes); computation-heavy process requiring high-grade hardware; and the rapid accumulation of error caused by inaccurate sensors. In the other words, due to all of these shortcomings, at the present time no indoor positioning technology has an overwhelming advantages that meets the requirements of human life and industrial development. Therefore, although researchers have been working on indoor positioning technologies for many years, they are still trying to overcome these challenges and design an indoor positioning system which can be widely used in human life.

Put another way, a single positioning system can be seen as a double-edged sword. When the system utilizes its positioning principles and corresponding measurements, the inherent shortcomings are usually unavoidable. Therefore, integration and customizability are possible solutions at this stage: by using different indoor positioning technologies in coordination, their advantages and shortcomings can be made complementary. In other words, choosing the appropriate technology depends on the environment, for which the integrated positioning system can be customized to achieve accurate, low-cost indoor positioning.

Geomatics can provide another approach for developing this technology by employing coordinate systems, an accurate map and floor plan, as well as positioning technology originally set up for surveying and mapping. In considering these approaches to the problem of indoor navigation, the author has developed some enhanced strategies using various positioning technologies, not only employing mainstream knowledge but also cutting-edge Geomatics. After applying these systems, the integrations and algorithms are verified and evaluated at various test sites.

To begin, this study proposes the use of integrated architecture for indoor positioning based on the sensors embedded in smartphones as well as other common positioning technologies, with the core approach based on inertial positioning technology. To verify this, different integrations and enhanced algorithms have been developed for this study’s integrated architecture: 1) walking velocity constraint and real-time smoothing for pedestrian inertial navigation; 2) map-aided pedestrian dead reckoning (PDR) using a fuzzy decision tree; 3) space resection aided PDR, and integration based on PDR and ANN aided marker-based image localization; 3) a differential Bluetooth positioning system. As preliminary verification, these strategies were evaluated for this study and found to be performing well, boding well for the success of the proposed ultimate integrated architecture. However, since there is a lot of related technology in this complex architectural system, numerous tasks await later research projects. The proposed integrated architecture can only be considered a truly excellent indoor positioning application after all of the remaining parts are finished and integrated. In addition, the specifications and performance of the sensors which were employed are important for developing corresponding technologies, as is the map, floor plan, and georeferenced images used in this study. Therefore, the calibrations of the sensors and the mobile mapping procedures are attached, and the experience of developing an indoor positioning system in a real site is presented. The purpose of this thesis is not only to make a technological contribution, but also to serve as a guide for those who which to work in the field of sensing and positioning.
論文目次 摘要 I
Abstract III
Acknowledgements VI
Table of Contents VIII
List of Tables XII
List of Figures XV
Glossary of Abbreviations XXIII
Table of Symbols XXVI
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Objective and Contributions 14
1.3 Thesis Outline 19
Chapter 2 Fundamentals of Related Positioning Algorithms 22
2.1 Inertial Navigation System 22
2.1.1 Types of Inertial Navigation System 23
2.1.2 Reference frames 25
2.1.3 Mechanization Equations 28
2.1.4 Integration with Global Navigation Satellite System 34
2.1.5 Filters for Integration 37
2.1.6 Smoothing Algorithms 40
2.1.7 Integration Architectures 42
2.1.8 Constraint Algorithms 45
2.2 Pedestrian Dead Reckoning 47
2.2.1 Step Detection 48
2.2.2 Step Length Estimation 52
2.2.3 Heading Estimation 55
2.2.4 Position Estimation and Integration 56
2.3 Bluetooth Positioning Algorithm 58
2.3.1 Wireless Positioning Methods 58
2.3.2 Bluetooth Low Energy 66
2.4 Image-based Localization 74
2.4.1 Types of Image-based Localization 76
2.4.2 The Use of Georeferenced Images 79
2.5 Artificial Neural Network aided Positioning Systems 81
2.5.1 The Evolution of Artificial Intelligence 82
2.5.2 Artificial Neural Network 84
2.6 Map Matching Algorithm 92
2.7 Fuzzy Logic and Decision Tree 97
2.7.1 Fuzzy Logic 97
2.7.2 Decision Tree 100
2.8 Summary 102
Chapter 3 Proposed Algorithms for An Inertial Navigation System 104
3.1 Methods 104
3.1.1 Walking Velocity Constraint 108
3.1.2 Indoor Real-time Smoothing 111
3.2 Experimental Plans 113
3.3 Results and Discussion 116
3.3.1 Walking Velocity Constraint 116
3.3.2 Indoor Real-time Smoothing 126
Chapter 4 Proposed Algorithms for Pedestrian Dead Reckoning 136
4.1 Basic Pedestrian Dead Reckoning 136
4.1.1 Methods 137
4.1.2 Experimental Plans 142
4.1.3 Results and Discussion 143
4.2 Map-aided Fuzzy Decision Tree 146
4.2.1 Methods 146
4.2.2 Experimental Plans 160
4.2.3 Results and Discussion 166
4.3 Space Resection aided Algorithm 185
4.3.1 Methods 185
4.3.2 Experimental Plans 195
4.3.3 Results and Discussions 198
4.4 Artificial Neural Network aided Image-based Algorithm 207
4.4.1 Methods 208
4.4.2 Experimental Plans 217
4.4.3 Results and Discussion 221
4.4.4 Special Issues: Catastrophic Forgetting of ANN 227
Chapter 5 Proposed Differential Bluetooth Positioning System 247
5.1 Methods 248
5.2 Experimental Plan 255
5.3 Results and Discussion 257
Chapter 6 Conclusion and Recommendations for Future Study 270
Appendix A: Sensor Calibrations 278
A.1 Inertial Measurement Unit 278
A.1.1 System Error Calibration 282
A.1.2 Random Error Analysis 284
A.1.3 Temperature Effect 288
A.1.4 Results and Summary 293
A.2 Magnetic Sensor 297
A.3 Camera 301
A.4 Bluetooth Beacon 305
Appendix B: Indoor Mobile Mapping 308
B.1 Portable MMS 310
B.2 Unmanned Aerial Vehicle MMS 313
B.3 Actual Case Related to This Study 316
Appendix C: Software Design 322
Appendix D: Guide to Building an Indoor Navigation System 326
Reference 331
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