DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Myung, Hyun | - |
dc.contributor.advisor | 명현 | - |
dc.contributor.author | Lee, Yu-Cheol | - |
dc.date.accessioned | 2021-05-12T19:39:00Z | - |
dc.date.available | 2021-05-12T19:39:00Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=908474&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/284089 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 로봇공학학제전공, 2020.2,[viii, 87 p. :] | - |
dc.description.abstract | This dissertation is regarding a method to estimate the global location of mobile robots and pedestrians by fusing Wi-Fi’s received signal strength indication (RSSI), dead-reckoning position information, and spatial map data in indoor environments. Recently, Wi-Fi routers, as typical pervasive network devices, have been widely installed in indoor spaces such as homes, offices, and shopping malls owing to the wide spread of ubiquitous computing. Wi-Fi provides communication functions | - |
dc.description.abstract | furthermore, its RSSI can be used as an invisible marker for indoor localization. In this study, I proposed two methods to improve issues in conventional localization methods of mobile robots and pedestrians by utilizing Wi-Fi RSSI based localization. The first method, hierarchical global localization framework, estimates the position of mobile robots by integrating the Wi-Fi RSSI-based fingerprint analysis method and an indoor grid map-based particle filter. This method determines the initialization region of a particle filter using fingerprint analysis results and estimates position by optimizing the sampling distribution. This method can solve the local minimum problem of particle filters and optimize the sampling size more effectively. The second method, sequential motion tracking, estimates a pedestrian’s position stably by combining the Wi-Fi RSSI-based fingerprint analysis method and indoor topological map motion information. This method estimates position by matching the moving trajectory of a topological map with the motion information accumulated using a dead-reckoning sensor in a global region discovered through fingerprint analysis results. This method can improve the continuous localization’s accuracy and the technique’s stability compared with the conventional methods such as the fingerprint analysis and the particle filter. Finally, the two proposed methods were applied to a mobile robot and a smartphone, separately, and their performances were investigated through actual localization test results. Furthermore, their effectiveness was verified based on comparative analysis with other methods. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | global robot localization▼apedestrian location tracking▼afingerprint analysis▼aradio map▼aparticle filter▼asample optimization▼asequential motion tracking▼atopological map | - |
dc.subject | 전역 로봇 위치인식▼a사용자 위치추종▼a핑거프린트 분석▼a전파지도▼a파티클 필터▼a샘플 최적화▼a연속 모션 트래킹▼a위상지도 | - |
dc.title | Hierarchical global localization with radio signals and spatial maps | - |
dc.title.alternative | 전파 신호와 공간 지도를 통한 계층적 전역 위치인식 기법 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :로봇공학학제전공, | - |
dc.contributor.alternativeauthor | 이유철 | - |
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