## 6 Conclusions and Future Work  
This paper presented an improved indoor positioning and obstacle recognition system for dark environments based on MC4L-IMU. To enhance recognition accuracy for objects with similar textures, we introduced FFT into the LBP-CNNs model, forming LBP-FFT-CNNs. This approach significantly improved obstacle identification accuracy. Additionally, we integrated an IMU with the MC4L device and designed an inertial-based hybrid indoor positioning algorithm. Experimental results demonstrated that the proposed LBP-FFT-CNNs model reduced the average indoor positioning error to 0.91 cm, with an obstacle recognition accuracy of 96.3%.  
Compared with traditional methods, the LBP-FFT-CNNs model outperformed LRA and LBP-CNNs in positioning accuracy and stability, achieving an R\({}^{2}\) value of 0.9949, the highest among all models. The integration of FFT effectively captured frequency domain features and leading to improved regression performance. Furthermore, the system demonstrated strong adaptability to different obstacle environments with the lowest error observed for low-reflectivity materials and stable performance under diverse conditions. Comparative analysis with state-of-the-art depth estimation methods confirmed that the LBP-FFT-CNNs model improved accuracy by 51.1% to 66.7%, underscoring its advantages in feature extraction and positioning reliability. Additionally, all models exhibited robust stability with PSI values below 0.02, ensuring consistent performance across various conditions.  
Future work will explore further improvements by integrating advanced sensor fusion techniques, such as incorporating depth cameras or LiDAR [31] to enhance obstacle recognition in more complex environments. Additionally, more sophisticated machine learning models, such as transformers or graph neural networks, will be leveraged. This could further improve recognition accuracy, particularly for objects with similar textures. Finally, large-scale field validation will be conducted to ensure broader applicability and consistent performance in real-world indoor environments.  
**Author Contributions:** Conceptualization, N.Y.; methodology, N.Y.; software, N.Y.; validation, J.-S.K.; formal analysis, N.Y.; investigation, N.Y.; resources, N.Y. and Y.S.; data curation, Y.S.; writing--original draft preparation, N.Y.; writing--review and editing, N.Y. and Y.S.; visualization, Y.S.; supervision, J.-S.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2016R1D1A1B02008553). This study was supported by the BK21 FOUR project (AI-driven Convergence Software Education Research Program) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (4199990214394).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.