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Indoor Space Localization with a Mobile Phone by Object Tracking
물체 추적을 응용한 핸드폰 실내 위치 인식 방법에 대한 연구

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dc.contributor.advisor이건우-
dc.contributor.author유수곤-
dc.date.accessioned2017-07-14T03:42:53Z-
dc.date.available2017-07-14T03:42:53Z-
dc.date.issued2016-08-
dc.identifier.other000000137047-
dc.identifier.urihttps://hdl.handle.net/10371/123914-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 8. 이건우.-
dc.description.abstractRecent researches on indoor localization have achieved a rapid progress, thanks to advances in mobile devices and networks. Related into simultaneous localization and mapping (SLAM) problems, several researchers apply different approaches, such as Wi-Fi, IMU sensors, and ultrasonic sensors. However, more intuitive and accessible system for indoor localization is required in order to achieve high-rate recognition of the current pose. In this paper, we propose the system that has the combination of visual data from a camera and inertial data from IMU sensors in indoor localization. Pre-learning of landmark images and setting up the database is the first part of our proposed localization method. Using TLD tracker and sensor data simultaneously, selected image areas are tracked and approximation of the device location can be extracted. EKF-SLAM, which uses extended Kalman filter to estimate device locations with sensor data, leads to real-scale estimation of the device approximately. From the characteristics of the camera and scale estimation from vision data and sensor data, camera poses are estimated and the landmark locations are matched. Even though abrupt changes of camera movement and angles cause errors on trajectories of the mobile device, camera pose estimation is successfully estimated, and the errors has a range from -0.1m to 0.4m, compared to the ground truth of the movement.-
dc.description.tableofcontentsChapter 1. Introduction 1

Chapter 2. Related Works 4

Chapter 3. System Overview 7

Chapter 4. Methods 10
4.1. Pre-learning 10
4.1.1. Landmark location dataset 10
4.1.2. Device orientation 12
4.1.3. Camera parameters 14
4.2. Object tracking and data acquisition 15
4.2.1. TLD tracker 15
4.2.2. Sensor data acquisition 16
4.2.3. EKF SLAM 19
4.3. Camera pose estimation 20
4.3.1. Structure from motion in multiple images 20
4.3.2. Camera back-projection 21
4.3.3. Scale factor estimation 23

Chapter 5. Results 24

Chapter 6. Conclusion 29

Bibliography 31

Abstract in Korean 35
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dc.formatapplication/pdf-
dc.format.extent753367 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectMonocular camera-
dc.subjectobject tracking-
dc.subjectindoor localization-
dc.subjectextended Kalman filter-
dc.subjectsimultaneous localization and mapping-
dc.subject.ddc621-
dc.titleIndoor Space Localization with a Mobile Phone by Object Tracking-
dc.title.alternative물체 추적을 응용한 핸드폰 실내 위치 인식 방법에 대한 연구-
dc.typeThesis-
dc.contributor.AlternativeAuthorSoogon Yoo-
dc.description.degreeMaster-
dc.citation.pagesvi, 36-
dc.contributor.affiliation공과대학 기계항공공학부-
dc.date.awarded2016-08-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Mechanical Aerospace Engineering (기계항공공학부)Theses (Master's Degree_기계항공공학부)
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