導航技術主要仰賴的全球衛星導航系統(Global Navigation Satellite System, GNSS)，由於衛星訊號會遭受建築物遮蔽，因此當使用者身處室內環境時，一套作為替代的完善室內定位系統是必要的。本論文在考量儀器功耗的情況下，利用ZigBee無線傳輸模組來建構室內無線感測網路，再搭配接收訊號強度(Received Signal Strength)環境特徵比對法(fingerprinting method)來建立基礎的室內定位系統，但由於訊號在室內環境容易出現反射與折射的現象，因此該定位系統精確度仍有改善空間。在此本論文選擇利用單攝影機的單眼影像同步定位與建圖技術（Monocular Simultaneous Localization and Mapping, MonoSLAM），藉由MonoSLAM提供速度資訊，作為改善fingerprinting室內定位系統的方法，當使用者手持攝影機拍攝時，MonoSLAM技術便利用擴展式卡爾曼濾波器(Extended Kalman Filter)從影片中估測使用者狀態，由於高頻率的拍攝影像與濾波器的幫助之下，將可獲得較原本定位系統精確且平滑的結果。最後本論文利用粒子濾波器(Particle Filter)將來自fingerprinting的定位結果與來自MonoSLAM的速度結果二者資訊整合輸出，並提出判斷步驟以確保MonoSLAM能持續給予正確的資訊，此研發之室內定位系統經由實際室內實驗結果驗證本論文提出的整合室內定位技術能成功改善室內定位系統效能。

Many applications for positioning and navigation services depend on global navigation satellite system (GNSS). Unfortunately, the GNSS cannot serve the indoor user because the signals from the satellites are blocked by buildings. The fingerprinting method based on the received signal strength (RSS) utilizes the pattern recognition to calculate the unknown user position in the indoor environment. However, the user dynamic states cannot be computed by the fingerprinting method. Monocular simultaneous localization and mapping (MonoSLAM) is an alternative method to provide the navigation service for the indoor user, and it estimates the user states through the changes in the video stream. The weakness of MonoSLAM is the accurate estimation of the camera direction changes. Because the estimated states of MonoSLAM are based on the previous states, the incorrect estimation would cause the divergence result with time. To complementary combine the fingerprinting method and the MonoSLAM method is the goal of the proposed integrated indoor positioning system. Thus, the particle filter is used in this work to utilize the velocity information determined by the MonoSLAM to improve the performance of fingerprinting positioning results, and an additional status check step is proposed to prevent any MonoSLAM failure. Finally, the experiment results of this thesis show that the integrated system reduces the indoor positioning error as well as the capability to correct the possible erroneous information from MonoSLAM.

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