近年來，適地性服務(Lacation Based Service, LBS)之興起，定位及導航已成為實踐智慧城市不可或缺的技術，目前最常見之定位技術為全球導航衛星系統(Global Navigation Satellite System, GNSS)與慣性導航系統(Inertial Navigation System, INS)，此技術已廣泛運用於室外定位。然而，室內定位之於室外定位是相對困難的，歸因於其衛星訊號易受到遮蔽而失去定位能力，又慣性感測器之誤差會隨著時間增加而累積，因此，需要以外部之輔助資訊來進行定位。現今，智慧型手機皆內建了高解析度數位相機，因此，本研究以智慧型手機之影像感測器，透過光線幾何交會之原理應用於室內定位，此方法不但成本低廉，更能快速獲得高精度室內之絕對位置。
影像辨識為影像定位不可或缺的一環，常見的方法為特徵定位與標誌定位。特徵定位 需要事先建立影像資料庫，並會增加計算量。因此，本研究以特製的定位標誌來進行室內定位，採用影像處理演算法辨識標誌上的二維條碼，當特定標誌被辨識出後，即可獲得其室內導航資訊，進而求解使用者之絕對位置。本研究所提出之演算法穩健性高，其辨識之有效距離大約6公尺，優於其他演算法之辨識距離。除此之外，該演算法除了能夠快速辨識外，亦適應於複雜之環境、能在未知環境中快速初始化。另一方面，本研究使用類神經網路(Artificial Neural Networks, ANN)進行位置估算，可使定位精度較傳統影像定位技術提升，其訓練模型亦可通用於不同場景，並符合室內定位精度之應用。

With the development of wireless communication technology, Location Based Service (LBS) has been a popular issue in recent years. To achieve the concept of smart city, positioning and navigation technologies are indispensable. The most common positioning techniques are Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), which are widely used in outdoor positioning. However, indoor positioning is more challenging due to the blocked satellite signal, which makes positioning disable in GNSS-hostile environment. Furthermore, the error of inertial sensors will accumulate with time. As a result, some external information is needed to realize indoor positioning. The research uses the smartphone camera and adopts the principle of light intersection to carry out indoor positioning. The proposed image-based positioning is not only low cost but also rapid to get the absolute position in indoor environment.
The main technique in traditional image-based positioning is image recognition including feature-based and marker-based positioning. Image database should be established in advance and cause computational burden in feature-based positioning. Hence, the research adopts the marker-based positioning, which uses self-made markers to carry out indoor positioning. Once the specific marker is recognized, the user can obtain the indoor navigation information from the corresponding marker. The proposed algorithm for recognition has high robustness and its effective distance is about 6 meters for which is farther than other marker recognition methods. It can avoid the effect caused by the environment or provide other pedestrians initial position in an unknown indoor environment. On the other hand, the research adopts Artificial Neural Networks (ANNs) to estimate the user position. Compared with other image-based positioning techniques, it can reach higher accuracy. The model can be used in different scene and achieve the accuracy of indoor positioning application.

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