近年來，隨著室內定位需求日益增加，越來越多整合裝置及相關演算法不斷推陳出新，有別於目前主流的室內定位裝置，如基於藍芽、Wi-Fi等需耗費大量架設成本的設備，本研究將室內定位平台構築於Intel RealSense T265 Tracking Camera與慣性量測元件(Inertial Measurement Unit, IMU)之多感測器整合技術開發，以低成本、低耗電量與穿戴式產品作為本研究之設計目標。其中，基於純IMU的觀測量，能藉由運動方程式之積分方法，獲得高更新率的位置及速度資訊。然而，因IMU本身含有未知量測偏差，而偏差量將藉由積分不斷累積，最終導致嚴重飄移現象。為了限制誤差累積所造成定位失準的狀況，將以行人航位推算(Pedestrian Dead Reckoning, PDR)法提供的位置與前進速度為依據，透過擴展式卡爾曼濾波器(Extended Kalman Filter, EKF)建立積分飄移補償模型，同時，也能將典型PDR更新率由2~5赫茲提高至100赫茲。然而，由於PDR為利用上一時刻位置、當前時刻估測的步長及航向角資訊，以累加方式解算當前時刻位置，因此，航向角誤差累積的缺點也將降低定位精度。有鑑於此，藉由T265 Camera提供穩定的速度與定位資訊能有效進行即時修正，同時，亦能當Camera定位失效時，於短時間依靠PDR定位結果進行補償。整體融合定位程序將藉由本研究設計之KF架構來實現，並以RealSense API提供Camera追蹤信心度(Tracker Confidence)作為設計KF權重之依據，以開發具補償與融合定位效果之演算流程。

With the increasing demand for indoor navigation applications, plenty of integrated devices have been rolled out to meet all the needs. Many existing indoor positioning methods rely on pre-installed sensor networks, such as Wi-Fi, Bluetooth, UWB, etc. Unlike these approaches, Intel RealSense T265 Tracking Camera and IMU will be integrated for establishing a portable navigation platform in this paper. From the practical point of view, it does not require an extra cost in volume and hardware upgrading. Above all, IMU mechanism provides a higher frequency update rate in positioning by integrating the gravity-free acceleration in the navigation frame (n-frame). However, the results are usually accompanied by significant accumulated errors with time due to the IMU sensor biases. In view of this, Pedestrian Dead Reckoning (PDR) can relatively provide more accurate pedestrian navigation solutions. Based on PDR position results and Zero-velocity Update (ZUPT) constrain, an Extended Kalman Filter (EKF)-based framework for identifying the drifts will be modeled to suppress the accumulated errors. On the other hand, the typical PDR with 2~5 Hz update rate can achieve a higher frequency output (e.g., 100Hz). With the aim of controlling the PDR position and velocity drifts thoroughly, using T265 Camera for positioning fusion is another flexible and stable approach. In addition, the PDR has a pivotal role in providing information for a short period of time, even when the camera signals are not available in some specific scenes. The above-mentioned integrated process will be realized under the KF framework developed in this paper. Moreover, the camera tracker confidence provided from RealSense API is considered as an index for KF weighting adjustment design.

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