e507c9a9-232f-4ab1-9cf2-83f0fa4fa0e020240916030539377wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS2224-34021790-083210.37394/23209http://wseas.org/wseas/cms.action?id=40461220241220242110.37394/23209.2024.21https://wseas.com/journals/isa/2024.phpYi-HsienLuDepartment of Graduate Institute of Network and Multimedia, National Taiwan University, TAIWANChia-ChihuangChia-ChihuangDepartment of Computer Science and Information Engineering, National Taiwan University, TAIWANChih-ChungChouDepartment of Computer Science and Information Engineering, National Taiwan University, TAIWANCheng-FuChouDepartment of Graduate Institute of Network and Multimedia, National Taiwan University, TAIWANSimultaneous Localization and Mapping (SLAM) technologies are indispensable for indoor service robots, enabling them to navigate through and interact with environments. Visual SLAM systems often encounter significant challenges such as dynamic obstacles, variable lighting, feature scarcity, and perceptual aliasing in real-world scenarios. By merging the precise environmental mapping capabilities of visual SLAM with the ubiquity and stability of WiFi signals, our method effectively addresses the limitations typically associated with visual SLAM. Notably, our fusion technique leverages existing WiFi infrastructure, thus providing a cost-effective improvement in spatial awareness without the extensive offline database requirements of WiFi RSSI-based localization. Comparative performance evaluations highlight that our graph optimization-based approach not only surpasses the original ORBSLAM3 method but also significantly outperforms the Extended Kalman Filter (EKF) in terms of accuracy, particularly in environments characterized by poor lighting, feature-less scenes, and significant occlusions. This is evidenced by a reduced Root Mean Square Error (RMSE) in localization: 3.09m for our method versus 4.02m for EKF. This enhancement in precision underscores the potential of our integrated system to advance indoor navigation technologies, making it a crucial development in the field of robotics and automated systems.93202493202439840837https://wseas.com/journals/isa/2024/a725109-022(2024).pdf10.37394/23209.2024.21.37https://wseas.com/journals/isa/2024/a725109-022(2024).pdf10.1109/tro.2021.3075644C. Campos, R. Elvira, J. J. Gomez, J. M. M. Montiel, and J. D. Tardós. ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multi-map SLAM. IEEE Transactions on Robotics, 37(6):1874–1890, 2021. C.-C. Chou. Enhance SLAM Performance with Tightly-Coupled Camera and Lidar Fusion. PhD thesis, National Taiwan University, Taipei, 2021. 10.1109/percom.2019.8767421M. Abbas, M. Elhamshary, H. Rizk, M. Torki, and M. Youssef. Wideep: Wifi-based accurate and robust indoor localization system using deep learning. In 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp.1–10. IEEE, 2019. 10.1007/s11749-016-0481-7G. Biau and E. Scornet. A random forest guided tour. Test, Springer Test, 25:197–227, 2016. 10.1109/ipin.2016.7743586A. H. Salamah, M. Tamazin, M. A. Sharkas, and M. Khedr. An enhanced wifi indoor localization system based on machine learning. In 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp.1–8, 2016. G. Welch, G. Bishop, et al. An introduction to the kalman filter. 1995. 10.1109/icra.2016.7487211J. Zhang, M. Kaess, and S. Singh. On degeneracy of optimization-based state estima- tion problems. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.809–816. IEEE, 2016. 10.3390/s20185182G. Lee, B.-C. Moon, S. Lee, and D. Han. Fusion of the slam with wi-fi-based positioning methods for mobile robot-based learning data collection, localization, and tracking in indoor spaces. Sensors, 20(18):5182, 2020. 10.1109/icaie53562.2021.00055C.-Z. Sun, B. Zhang, J.-K. Wang, and C.-S. Zhang. A review of visual slam based on unmanned systems. In 2021 2nd International Conference on Artificial Intelligence and Education (ICAIE), pp.226- 234. IEEE, 2021. 10.1109/ipin.2016.7743586A. H. Salamah, M. Tamazin, M. A. Sharkas, and M. Khedr. An enhanced wifi indoor localization system based on machine learning. In 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp.1–8, 2016. 10.1109/iros.2012.6385773J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers. A benchmark for the evaluation of rgb-d slam systems. In Proc. of the International Conference on Intelligent Robot Systems (IROS), Oct. 2012. 10.1109/wocc53213.2021.9602896Y.-H. Wang, T.-W. Yang, C.-F. Chou, and I.- C. Chang. Wi-fi dsar: Wi-fi based indoor localization using denoising supervised autoencoder. In 2021 30th Wireless and Optical Communications Conference (WOCC), pp.188-192, 2021. 10.1109/lra.2020.2970665Yongbo Chen, Liang Zhao , Ki Myung Brian Lee. Broadcast Your Weakness: Cooperative Active Pose-Graph SLAM for Multiple Robots In 2020 IEEE Robotics and Automation Letters, Morgan Quigley,Ken Conley,Brain PGerkey.ROS: an open-source Robot Operating System. ICRA 2029 Workshop on Open Source Software. 10.1109/jsen.2017.2660522W. Xue, W. Qiu, X. Hua, and K. Yu. Improved wi-fi rssi measurement for indoor localization. IEEE Sensors Journal, 17(7):2224–2230, 2017.