Indoor positioning technology plays an important role in improving efficiency and automating industrial processes such as warehouse management, production lines, and mobile robot navigation. However, existing RFID-based and odometry-only localization methods still suffer from limited accuracy, drift, and dependence on predefined infrastructure. To address these challenges, this work proposes a lightweight sensor fusion framework that combines wheel encoder data and phase-based RFID signals using an Extended Kalman Filter (EKF) for accurate indoor localization of mobile robots. The proposed method does not require prior knowledge of the tag map and enables convergence even when the robot starts outside the reader's range. Simulation results demonstrate that the fused method achieves an average positioning error of 5.4 cm and a final error of less than 8 cm. An ablation study comparing odometry-only, RFID-only, and fusion scenarios confirms the superiority of the integrated approach in terms of accuracy and robustness. The system is suitable for real-time implementation in cost-effective embedded platforms and has potential for deployment in smart warehouses and logistics environments.