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系統識別號 U0026-0906202010212800
論文名稱(中文) 手機即時室內定位系統基於單張影像後方交會和行人航位推算
論文名稱(英文) Real Time Indoor Positioning Based on Image Resection and PDR
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) 蘇玫甄
研究生(英文) Mei-Chen Su
電子信箱 f64031041@gmail.com
學號 P66074044
學位類別 碩士
語文別 英文
論文頁數 96頁
口試委員 指導教授-江凱偉
口試委員-詹劭勳
口試委員-卓大靖
中文關鍵字 手機即時室內定位  影像定位  影像辨識  影像匹配  後方交會  行人航位推算 
英文關鍵字 Real Time Indoor Positioning on Smartphone  VisionBased Positioning  Image Recognition  Image Matching  Resection  Pedestrian Dead Reckoning 
學科別分類
中文摘要 隨著手機導航與定位普及於日常生活中,適地性服務(Location Based Service, LBS)因此變成熱門議題,目前於室外環境下可利用全球衛星定位系統(Global Navigation Satellite System, GNSS)結合慣性導航系統(Inertial Navigation System, INS)和一些地圖約制的演算法來達成獲得位置資訊的目標。然而室內因遮蔽問題無法接收到衛星訊號,為了使LBS在室內環境下也能延續作用,故各項室內定位技術開始發展起來。其中基於視覺辨識的定位,因不需要基礎建設且精度可達到期望的水準,較具備競爭力。隨著科技進步,每台智慧型手機都配備相機功能,手機的計算能力、圖像處理能力都能克服視覺辨識處理時間過長的問題,在影像定位的前提下,此方法可以在較少的時間定位出準確的位置。
因此本研究設計了一個應用程式,利用設置方框在自然景物周圍的方式來大幅減少影像處理時間,可在離線階段利用已事先進行過相機率定的手機,結合開放式資料庫OpenCV中的Canny邊際萃取、輪廓偵測等演算法,使手機可以快速尋找到框的位置,並與資料庫進行ORB影像匹配,提取出在資料庫中框內自然地物的區域坐標,並利用單張影像後方交會的原理來進行室內定位,此方法可以在不利用硬體設備的情況下有效的進行即時影像室內定位,且誤差也不隨時間累積,其可達公分等級的精度。並將此技術搭配行人航位推算 (Pedestrian Dead Reckoning, PDR),計算出連續的使用者位置資訊並減低整體的運算負荷,展示於室內地圖上,透過影像定位來校正行人航位推算隨時間累積的誤差,結合兩技術的優點,達成快速精準的室內定位技術。該演算法可以在離線狀態有效地於資料庫中匹配物件,並達到公分級的定位精度,為一種低成本、高處理速度和高精準度的室內定位技術。
英文摘要 An astonishing growth of smartphone usage and real time navigation popularity have been witnessed in recent years. Hence, Location Based Service (LBS) has become increasingly important for a rising number of applications in different field. In outdoor environments, the Global Navigation Satellite System (GNSS) can be exploited in conjunction with Inertial Navigation Systems (INS) and some algorithms of map constraint to achieve the goal for obtaining the precise position information. However, in indoor environments, satellite signal cannot be received due to the shadowing problem, so that indoor positioning technique should seek out other technologies to get location information. Consequently, various indoor positioning technologies began to develop and lots of service are provided nowadays. Particularly, vision-based technology has main advantages including the infrastructure is not necessary and the accuracy has reached appreciable levels. If the time burden of image processing can be improved, vision-based has a good prospect since the growth of computational capabilities, image processing, and the popularity of smartphone built-in camera.
Therefore, the research designs an application on mobile phone, not only attaches markers on object to decrease processing time, but also does camera calibration to improve the accuracy in advance. In addition, the research develops an algorithm assisted by Canny and contour detection algorithm of OpenCV library to find markers effectively. In offline stage, the application has constructed a database of control points in object coordinate system and the coefficient of Interior Orientation Parameters (IOPs). Under these circumstances, user just takes one photo then the application can detect those markers and doing ORB matching with RANSAC to retrieve those attributes according objects from database. The application can successfully match objects from database and through space resection to achieve indoor positioning in centimeter-level. Next, vision-based positioning combines with Pedestrian Dead Reckoning (PDR) to calculate continuous user’s position and reduce the overall computational burden, then display user trajectory on the indoor map. Furthermore, vision-based positioning can calibrate the error which increases over time and produced by PDR and take their respective advantages to achieve rapid and accurate indoor positioning technology. Overall, the indoor positioning technology which is proposed by the research is a low cost, high processing speed and high accuracy real time method.
論文目次 Content
摘要 ........................................................................................................................................... I
Abstract .................................................................................................................................. III
致謝 .......................................................................................................................................... V
Content ................................................................................................................................... VI
List of Tables......................................................................................................................... IX
List of Figures ......................................................................................................................... X
Chapter 1 Introduction................................................................................................ 1
1.1 Motivation and Problem Statement ............................................................................... 1
1.2 Objectives and Contributions....................................................................................... 3
1.3 Thesis Outline................................................................................................................... 4
Chapter 2 Background................................................................................................. 6
2.1 Background to Radio Frequency (RF) based indoor navigation on smartphone..... 6
2.1.1 Wireless Local Area Networks (WLAN) based positioning................................... 7
2.1.2 Bluetooth Low Energy (BLE) based positioning...................................................... 9
2.1.3 Summary ...................................................................................................................... 10
2.2 Background to Vision-based Indoor Positioning ....................................................... 11
2.3 Background to Inertial Navigation (self-contained sensor)...................................... 13
2.4 Background to Magnetic Field Magnitude (MFM) based Indoor Positioning....... 14
Chapter 3 Vision-based Indoor Positioning Assisted with PDR....................... 16
3.1 Coordinate System......................................................................................................... 16
3.1.1 Object Coordinate System ......................................................................................... 17
3.1.2 Camera Coordinate System ....................................................................................... 19
3.1.3 Digital Image Coordinate System............................................................................. 20
3.1.4 Sensor Coordinate System......................................................................................... 22
3.2 Application System Architecture ................................................................................. 23
3.3 Database Introduction.................................................................................................... 25
3.3.1 SQLite........................................................................................................................... 26
3.3.2 Database Design.......................................................................................................... 27
3.4 Camera Calibration ........................................................................................................ 29
3.5 Space Resection.............................................................................................................. 31
3.6 Precision Indexes............................................................................................................ 34
3.7 Pedestrian Dead Reckoning Algorithm....................................................................... 36
Chapter 4 Image Processing ..................................................................................... 40
4.1 Marker-based and Feature-based Positioning............................................................. 40
4.2 The Flow Chart of Image Processing .......................................................................... 41
4.3 OpenCV Introduction .................................................................................................... 42
4.4 Grayscale ......................................................................................................................... 43
4.5 Gaussian Blur.................................................................................................................. 45
4.6 Canny Edge Detection ................................................................................................... 47
4.7 Find Contour ................................................................................................................... 49
4.8 Image Matching.............................................................................................................. 53
4.8.1 SIFT .............................................................................................................................. 54
4.8.2 SURF ............................................................................................................................ 54
4.8.3 ORB .............................................................................................................................. 55
4.9 Image Retrieval............................................................................................................... 56
4.10 Lens Distortion Calibration ........................................................................................ 58
4.11 Positioning Algorithm ................................................................................................. 61
Chapter 5 Results and Analysis................................................................................ 62
5.1 The Interface of Application System on Mobile Phone............................................ 62
5.2 Experiment Place............................................................................................................ 63
5.3 Camera Calibration Result and Lens Distortion ........................................................ 65
5.4 Database Interface .......................................................................................................... 68
5.5 Vision-based Indoor Positioning Result...................................................................... 69
5.5.1 Image Processing and Matching Result ................................................................... 71
5.5.2 Space Resection Accuracy Evaluation..................................................................... 74
5.6 PDR Trajectory Result...................................................................................................... 76
Chapter 6 Conclusions and Recommendations.................................................... 86
6.1 Conclusions..................................................................................................................... 86
6.2 Thesis Contributions ...................................................................................................... 87
6.3 Recommendations .......................................................................................................... 88
Reference ............................................................................................................................... 90
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