隨著工業4.0 與智慧物流的蓬勃發展，自主移動機器人已成為自動化倉儲與室內配送的核心技術。在動態複雜的室內環境中，單一感測器容易導致定位漂移、長時間運行累積誤差等問題。針對上述挑戰，本研究利用擴展卡爾曼濾波器進行感測器融合，提供高頻位姿估計。此外，利用因子圖框架整合多感測器資訊及視覺標記定位結果，在估計自走車位置的同時，也對視覺標籤位置進行校正。場景辨識部分，提出語義感知辨識機制，利用語意分割模型理解輸入影像，使系統能自動判別自走車當前所處區域，並動態切換對應之局部地圖。最後，構建一套完整的室內自走車定位系統，並於真實場景中驗證其可行性。實驗結果顯示，本系統在結構相似的室內環境下，能維持穩定的定位精度，且於場景切換後，透過視覺標籤的輔助，具備快速重定位的能力，整體定位強健性較傳統方法顯著提升，為智慧倉儲與室內服務機器人之實際部署提供可靠的技術基礎。

With the rapid development of Industry 4.0 and intelligent logistics, autonomous mobile robots have become a core technology in automated warehousing and indoor delivery. In dynamic and complex indoor environments, relying on a single sensor often leads to localization drift and accumulated errors over extended operation. To address these challenges, this study employs an Extended Kalman Filter (EKF) for sensor fusion to provide high-frequency pose estimation. Furthermore, a factor graph framework is adopted to integrate multi-sensor information and fiducial marker localization results, enabling simultaneous estimation of the robot's pose and correction of fiducial marker positions. For scene recognition, we propose a semantic perception mechanism, leveraging a semantic segmentation model to interpret input images, allowing the system to automatically determine the robot's current operational zone and dynamically switch to the corresponding local map. Finally, a complete indoor robot localization system is constructed and validated in real-world environments. Experimental results demonstrate that the proposed system maintains stable localization accuracy in structurally similar indoor environments. Moreover, with the assistance of fiducial markers, the system exhibits rapid relocalization capability after scene transitions. The overall localization robustness is significantly improved compared to conventional methods, providing a reliable technical foundation for practical deployment in smart warehousing and indoor service robotics.

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