SENSOR FUSION TECHNOLOGY ADVANCEMENT IN GPS-AIDED LOCALIZATION FOR AUTONOMOUS MOBILE ROBOTS: A COMPREHENSIVE SURVEY
DOI:
https://doi.org/10.11113/jurnalteknologi.v88.24026Keywords:
GPS accuracy, outdoor localization, sensor fusion, sensor integration, autonomous mobile robot, localization algorithmAbstract
Autonomous technology enables mobile robots to perform multiple functions, including navigation, decision-making, and automatic control, using sensors and advanced software. Localization, a key element of navigation, involves determining mobile robots’ precise location and orientation. As most of the outdoor robots utilize Global Positioning System (GPS)-based data to navigate, this study surveys advancements in GPS (Global Positioning System)-assisted localization for autonomous mobile robots focusing on sensor fusion technology. The methodology includes collecting and analyzing papers from 2018 to 2024 using keywords such as GPS accuracy improvement, autonomous navigation, outdoor localization, autonomous vehicle, and autonomous mobile robot. The classification and examination of the chosen papers offer a comprehensive overview of the advantages and disadvantages of sensors and methods used to improve GPS accuracy, and the evaluation of these sensors and methods to identify the optimal solution available. Notably, several sensor fusion approaches have demonstrated substantial improvements, for instance, reducing localization errors from 79 to 3.7 meters which thereby highlighting the study’s practical significance. The findings also indicate that visual sensors and fiducial markers are potential options to mitigate GPS signal loss, advanced filtering algorithms provide better accuracy and reliability, and real-time adaptive systems improve performance under various conditions, ensuring more reliable navigation. The integration of sensor fusion and advanced algorithms will provide significant technological progress in autonomous systems and intelligent environments.
References
Panigrahi, P. K., and S. K. Bisoy. 2022. Localization Strategies for Autonomous Mobile Robots: A Review. Journal of King Saud University – Computer and Information Sciences. 34(8): 6019–39. https://doi.org/10.1016/j.jksuci.2021.02.015.
Harun, M. H., S. S. Abdullah, M. S. M. Aras, and M. B. Bahar. 2022. Sensor Fusion Technology for Unmanned Autonomous Vehicles (UAV): A Review of Methods and Applications. In Proceedings of the 2022 IEEE 9th International Conference on Underwater System Technology: Theory and Applications (USYS), 1–8. IEEE. https://doi.org/10.1109/USYS56283.2022.10072667.
Raveena, C. S., R. S. Sravya, R. V. Kumar, and A. Chavan. 2020. Sensor Fusion Module Using IMU and GPS Sensors for Autonomous Car. In Proceedings of the 2020 IEEE International Conference for Innovation in Technology (INOCON). IEEE. https://doi.org/10.1109/INOCON50539.2020.9298316.
Hegarty, C. J., J. M. Foley, and S. K. Kalyanaraman. 2017. Global Positioning System. International Journal of Computer Trends and Technolog 46(2): 4–24. https://doi.org/10.1201/b17545.
Vatansever, S., and I. Butun. 2017. A Broad Overview of GPS Fundamentals: Now and Future. In Proceedings of the 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 1–6. https://doi.org/10.1109/CCWC.2017.7868373.
Ross, R., and R. Hoque. 2020. Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications. Applied Sciences. 10(1): 146. https://doi.org/10.3390/app10010146.
Yu, Z., Y. Hu, and J. Huang. 2018. GPS/INS/Odometer/DR Integrated Navigation System Aided with Vehicular Dynamic Characteristics for Autonomous Vehicle Application. IFAC-PapersOnLine. 51(31): 936–42. https://doi.org/10.1016/j.ifacol.2018.10.060.
Aydin, T., and E. Erdem. 2023. Novel Deep Hybrid and Ensemble Algorithms for Improving GPS Navigation Positioning Accuracy. IEEE Access. 11: 53518–30. https://doi.org/10.1109/ACCESS.2023.3272057.
Ngoc Huy, T., L. Manh Cam, and N. Thanh Nam. 2020. GPS/INS Integrated Navigation System for Autonomous Robots. Science and Technology Development Journal – Engineering and Technology. 3(SI1). https://doi.org/10.32508/stdjet.v3iSI1.720.
Khosyi’in, M., S. A. D. Prasetyowati, Z. Nawawi, and B. Y. Suprapto. 2019. Review and Design of GPS-RFID Localization for Autonomous Vehicle Navigation. In ACM International Conference Proceeding Series. 42–46. https://doi.org/10.1145/3362752.3362766.
Baxevani, K., I. Yadav, Y. Yang, M. Sebok, H. G. Tanner, and G. Huang. 2022. Resilient Ground Vehicle Autonomous Navigation in GPS-Denied Environments. Guidance, Navigation and Control. 2(4): 1–17. https://doi.org/10.1142/S2737480722500200.
Duan, B. 2024. Sensor and Sensor Fusion Technology in Autonomous Vehicles. Applied and Computational Engineering. 52(1): 132–37. https://doi.org/10.54254/2755-2721/52/20241470.
Fan, C., S. Wei, and Y. Yang. 2024. Application of Multi-Sensor Fusion Precise Positioning and Autonomous Navigation Technology in Substation Intelligent Inspection Robot. In Proceedings of the 2024 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE). IEEE. https://doi.org/10.1109/EDPEE61724.2024.00122.
Janevski, N. 2022. Performance of Sensor Fusion for Vehicular Applications. Master’s thesis, West Virginia University. https://researchrepository.wvu.edu/etd/11261.
Bai, Y., and C. Zhang. 2023. Multi-Sensor Information Fusion Algorithm Based on Adaptive Background Information. In Proceedings of the 2023 International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII). 367–70. https://doi.org/10.1109/ICMIII58949.2023.00076.
Yousuf, S., and M. B. Kadri. 2020. Information Fusion of GPS, INS and Odometer Sensors for Improving Localization Accuracy of Mobile Robots in Indoor and Outdoor Applications. Robotica. 39(2): 250–76. https://doi.org/10.1017/S0263574720000351.
Giménez-Espert, M. del C., and V. J. Prado-Gascó. 2019. Bibliometric Analysis of Six Nursing Journals from the Web of Science, 2012–2017. Journal of Advanced Nursing. 75(3): 543–54. https://doi.org/10.1111/jan.13868.
Liu, Y., X. Fan, C. Lv, J. Wu, L. Li, and D. Ding. 2018. An Innovative Information Fusion Method with Adaptive Kalman Filter for Integrated INS/GPS Navigation of Autonomous Vehicles. Mechanical Systems and Signal Processing. 100: 605–16. https://doi.org/10.1016/j.ymssp.2017.07.051.
Li, Z., D. Zhou, and Y. Huang. 2018. Design of Outdoor Following Vehicle System Based on GPS-INS Fusion Navigation Algorithm. In Proceedings of the 2018 IEEE Advanced Information Management, Communication, Electronic and Automation Control Conference (IMCEC). 1285–89. https://doi.org/10.1109/IMCEC.2018.8469395.
Zein, Y., M. Darwiche, and O. Mokhiamar. 2018. GPS Tracking System for Autonomous Vehicles. Alexandria Engineering Journal. 57(4): 3127–37. https://doi.org/10.1016/j.aej.2017.12.002.
Cai, H., Z. Hu, G. Huang, D. Zhu, and X. Su. 2018. Integration of GPS, Monocular Vision, and High-Definition Map for Accurate Vehicle Localization. Sensors. 18(10): 3270. https://doi.org/10.3390/s18103270.
de Winter, A., and S. Baldi. 2018. Real-Life Implementation of a GPS-Based Path-Following System for an Autonomous Vehicle. Sensors. 18(11): 3940. https://doi.org/10.3390/s18113940.
Wang, G., P. H. Joo Chong, and B. C. Seet. 2018. The Vehicle Trajectory Estimation Method Based on Information Fusion. In Proceedings of the 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), 1271–74. https://doi.org/10.1109/ITOEC.2018.8740664.
Li, T., H. Zhang, Z. Gao, Q. Chen, and X. Niu. 2018. High-Accuracy Positioning in Urban Environments Using Single-Frequency Multi-GNSS RTK/MEMS-IMU Integration. Remote Sensing. 10(2): 205. https://doi.org/10.3390/rs10020205.
Park, W. J., et al. 2018. Low-Cost MEMS-IMU-Based DR/GPS Integrated System in Urban Environment. In Proceedings of the International Conference on Control, Automation and Systems. 767–71.
Wang, X., and M. Liang. 2018. GPS Positioning Method Based on Kalman Filtering. In Proceedings of the 2018 International Conference on Robots & Intelligent System (ICRIS). 77–80. IEEE. https://doi.org/10.1109/ICRIS.2018.00028.
Taguchi, S., and T. Yoshimura. 2018. Robust Bayesian Filtering for Positioning Using GPS and INS in Multipath Environments. In Proceedings of the IEEE/ION Position, Location and Navigation Symposium (PLANS). 816–21. https://doi.org/10.1109/PLANS.2018.8373458.
Tu, Z., T. Lu, and Q. Chen. 2018. A Novel Carrier Loop Based on Unscented Kalman Filter Methods for Tracking High Dynamic GPS Signals. In Proceedings of the 2018 IEEE International Conference on Communication Technology, 1007–12.
Zhang, M., X. Jia, B. Yang, X. Chi, and D. Peng. 2018. A GPS Positioning Algorithm Based on Distributed Kalman Filter Data Fusion with Feedback. In Proceedings of the 37th Chinese Control Conference (CCC). 7359–63. https://doi.org/10.23919/ChiCC.2018.8483718.
Deng, Y., Y. Shan, Z. Gong, and L. Chen. 2018. Large-Scale Navigation Method for Autonomous Mobile Robot Based on Fusion of GPS and LiDAR SLAM. In Proceedings of the Chinese Automation Congress (CAC). 3145–48. https://doi.org/10.1109/CAC.2018.8623646.
Zhang, H., et al. 2019. A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy. Sensors. 19(7): 1623. https://doi.org/10.3390/s19071623.
Jung, H., J.-H. Park, and H.-Y. Jeong. 2019. Experimental Assessment of GNSS-Based Vehicle Positioning Accuracy Using 3-D SLAM Reference. In Proceedings of the IEEE 90th Vehicular Technology Conference (VTC2019-Fall). 1–2. https://doi.org/10.1109/VTCFall.2019.8891170.
Park, W. J., et al. 2019. MEMS 3D DR/GPS Integrated System for Land Vehicle Application Robust to GPS Outages. IEEE Access. 7: 73336–48. https://doi.org/10.1109/ACCESS.2019.2920095.
Shokri, S., and M. R. Mosavi. 2019. A Fuzzy Weighted Kalman Filter for GPS Positioning Precision Enhancement. In Proceedings of the 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). 1–5. https://doi.org/10.1109/CFIS.2019.8692157.
Pei, Y., S. Gao, G. Hu, Y. Zhao, and K. Jia. 2019. Mahalanobis Distance–Based Adaptive Unscented Kalman Filter and Its Application in GPS/MEMS-IMU Integration. In Proceedings of the 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 2649–55. https://doi.org/10.1109/ITNEC.2019.8729251.
Anbu, N. Allan, and D. Jayaprasanth. 2019. Integration of Inertial Navigation System with Global Positioning System Using Extended Kalman Filter. In Proceedings of the 2nd International Conference on Smart Systems and Inventive Technology (ICSSIT). 789–94. https://doi.org/10.1109/ICSSIT46314.2019.8987949.
Almeida, H. P., C. L. N. Junior, D. S. dos Santos, and M. C. R. Leles. 2019. Autonomous Navigation of a Small-Scale Ground Vehicle Using Low-Cost IMU/GPS Integration for Outdoor Applications. In Proceedings of the 13th Annual IEEE International Systems Conference (SysCon). https://doi.org/10.1109/SYSCON.2019.8836794.
Dhongade, A. P., and M. A. Khandekar. 2019. GPS and IMU Integration on an Autonomous Vehicle Using Kalman Filter (LabVIEW Tool). In Proceedings of the 2019 International Conference on Intelligent Computing and Control Systems (ICCS). 1122–25. IEEE. https://doi.org/10.1109/ICCS45141.2019.9065851.
Cai, G.-S., H.-Y. Lin, and S.-F. Kao. 2019. Mobile Robot Localization Using GPS, IMU, and Visual Odometry. In Proceedings of the 2019 International Automatic Control Conference (CACS). 1–6. IEEE. https://doi.org/10.1109/CACS47674.2019.9024731.
Menna, B., S. Villar, and G. Acosta. 2019. Navigation System for MACÁBOT: An Autonomous Surface Vehicle Using GPS-Aided Strapdown Inertial Navigation System. IEEE Latin America Transactions. 17(6): 1009–19. https://doi.org/10.1109/TLA.2019.8896824.
Cai, G. S., H. Y. Lin, and S. F. Kao. 2019. Mobile Robot Localization Using GPS, IMU, and Visual Odometry. In Proceedings of the 2019 International Automatic Control Conference (CACS). Chiayi, Taiwan: IEEE. https://doi.org/10.1109/CACS47674.2019.9024731.
Gharajeh, M. S., and H. B. Jond. 2020. Hybrid Global Positioning System–Adaptive Neuro-Fuzzy Inference System–Based Autonomous Mobile Robot Navigation. Robotics and Autonomous Systems. 134: 103669. https://doi.org/10.1016/j.robot.2020.103669.
Sahloul, S., D. Ben Halima Abid, and C. Rekik. 2021. A Hybridization of Global–Local Methods for Autonomous Mobile Robot Navigation in Partially Known Environments. Journal of Robotics and Control. 2(4): 221–33. https://doi.org/10.18196/jrc.2483.
Li, N., L. Guan, and Y. Gao. 2020. A Seamless Indoor and Outdoor Low-Cost Integrated Navigation System Based on LiDAR/GPS/INS. In Proceedings of the IEEE Vehicular Technology Conference (VTC 2020-Fall). https://doi.org/10.1109/VTC2020-Fall49728.2020.9348869.
Perea-Strom, D., A. Morell, J. Toledo, and L. Acosta. 2020. GNSS Integration in the Localization System of an Autonomous Vehicle Based on Particle Weighting. IEEE Sensors Journal. 20(6): 3314–23. https://doi.org/10.1109/JSEN.2019.2955210.
Bai, M., Y. Huang, Y. Zhang, and G. Jia. 2020. A Novel Progressive Gaussian Approximate Filter for Tightly Coupled GNSS/INS Integration. IEEE Transactions on Instrumentation and Measurement. 69(6): 3493–3505. https://doi.org/10.1109/TIM.2019.2932155.
Al Bitar, N., and A. I. Gavrilov. 2020. Neural Networks–Aided Unscented Kalman Filter for Integrated INS/GNSS Systems. In Proceedings of the 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS). 1–4. IEEE. https://doi.org/10.23919/ICINS43215.2020.9133878.
Yan, F., S. Li, E. Zhang, and Q. Chen. 2020. An Intelligent Adaptive Kalman Filter for Integrated Navigation Systems. IEEE Access. 8: 213306–213317. https://doi.org/10.1109/ACCESS.2020.3040433.
Aslinezhad, M., A. Malekijavan, and P. Abbasi. 2020. ANN-Assisted Robust GPS/INS Information Fusion to Bridge GPS Outage. EURASIP Journal on Wireless Communications and Networking. 2020(1). https://doi.org/10.1186/s13638-020-01747-9.
Li, W., X. Cui, and M. Lu. 2020. High-Precision Positioning and Mapping Using Feature-Based RTK/LiDAR/INS Integrated System for Urban Environments. In Proceedings of the 33rd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2020). 2628–2640. https://doi.org/10.33012/2020.17745.
Shokri, S., N. Rahemi, and M. R. Mosavi. 2020. Improving GPS Positioning Accuracy Using Weighted Kalman Filter and Variance Estimation Methods. CEAS Aeronautical Journal. 11(2): 515–27. https://doi.org/10.1007/s13272-019-00433-x.
Alzyout, M. S., and M. A. Alsmirat. 2020. Performance of Design Options of Automated ARIMA Model Construction for Dynamic Vehicle GPS Location Prediction. Simulation Modelling Practice and Theory. 104: 102148. https://doi.org/10.1016/j.simpat.2020.102148.
Hu, G., B. Gao, Y. Zhong, and C. Gu. 2020. Unscented Kalman Filter with Process Noise Covariance Estimation for Vehicular INS/GPS Integration System. Information Fusion. 64: 194–204. https://doi.org/10.1016/j.inffus.2020.08.005.
Benzerrouk, H., R. Landry, V. Nebylov, and A. Nebylov. 2020. Robust INS/GPS Coupled Navigation Based on Minimum Error Entropy Kalman Filtering. In Proceedings of the 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS 2020). https://doi.org/10.23919/ICINS43215.2020.9133871.
Chikurtev, D., N. Chivarov, S. Chivarov, and A. Chikurteva. 2021. Mobile Robot Localization and Navigation Using LiDAR and Indoor GPS. IFAC-PapersOnLine. 54(13): 351–56. https://doi.org/10.1016/j.ifacol.2021.10.472.
Zhi, Z., D. Liu, and L. Liu. 2022. A Performance Compensation Method for GPS/INS Integrated Navigation System Based on CNN–LSTM during GPS Outages. Measurement. 188: 110516. https://doi.org/10.1016/j.measurement.2021.110516.
Quan, D. Y., and W. F. Ying. 2021. An Outdoor GPS Navigation Optimization Method Based on Naïve Bayes Method. In Proceedings of the IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER 2021). 311–16. https://doi.org/10.1109/CYBER53097.2021.9588263.
Zohari, M. H., M. Hakimi, B. Zohari, M. F. Bin, and M. Nazri. 2021. GPS-Based Vehicle Tracking System. International Journal of Scientific & Technology Research. 10(4): 278–82.
Chen, Y., W. Li, and Y. Wang. 2021. A Robust Adaptive Indirect In-Motion Coarse Alignment Method for GPS/SINS Integrated Navigation System. Measurement. 172: 108834. https://doi.org/10.1016/j.measurement.2020.108834.
Wang, J., Z. Ma, and X. Chen. 2021. Generalized Dynamic Fuzzy Neural Network Model Based on Multiple Fading Factors SCKF and Its Application in Integrated Navigation. IEEE Sensors Journal. 21(3): 3680–93. https://doi.org/10.1109/JSEN.2020.3022934.
Al Bitar, N., and A. Gavrilov. 2021. A New Method for Compensating the Errors of Integrated Navigation Systems Using Artificial Neural Networks. Measurement. 168: 108391. https://doi.org/10.1016/j.measurement.2020.108391.
Correa-Caicedo, P. J., A. I. Barranco-Gutiérrez, E. I. Guerra-Hernandez, P. Batres-Mendoza, J. A. Padilla-Medina, and H. Rostro-González. 2021. An FPGA-Based Architecture for Latitude and Longitude Correction in Autonomous Navigation Tasks. Measurement. 182: 109757. https://doi.org/10.1016/j.measurement.2021.109757.
Li, W., G. Liu, X. Cui, and M. Lu. 2021. Feature-Aided RTK/LiDAR/INS Integrated Positioning System with Parallel Filters in the Ambiguity–Position Joint Domain for Urban Environments. Remote Sensing. 13(10): 2013. https://doi.org/10.3390/rs13102013.
Correa-Caicedo, P. J., A. I. Barranco-Gutiérrez, E. I. Guerra-Hernandez, P. Batres-Mendoza, J. A. Padilla-Medina, and H. Rostro-González. 2021. GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles. Mathematics. 9(21). https://doi.org/10.3390/math9212818.
Mukherjee, S., R. Kumar, and S. Borah. 2021. Obstacle-Avoiding Intelligent Algorithm for Quad Wheel Robot Path Navigation. International Journal of Intelligent Unmanned Systems. 9(1): 29–41. https://doi.org/10.1108/IJIUS-12-2019-0074.
Mu, X., B. He, S. Wu, X. Zhang, Y. Song, and T. Yan. 2021. A Practical INS/GPS/DVL/PS Integrated Navigation Algorithm and Its Application on Autonomous Underwater Vehicle. Applied Ocean Research. 106: 102441. https://doi.org/10.1016/j.apor.2020.102441.
Borah, S., R. Kumar, S. Mukherjee, F. C. Panwala, and A. P. Lakshmi. 2022. An Experimental Analysis of Quad Wheel Autonomous Robot Location and Path Planning Using Borahsid Algorithm with GPS and ZigBee. International Journal of Vehicle Information and Communication Systems. 7(3): 290. https://doi.org/10.1504/IJVICS.2022.127405.
Haider, M. H., et al. 2022. Autonomous Mobile Robot Navigation Using Adaptive Neuro-Fuzzy Inference System. In Proceedings of the 2022 International Conference on Innovation and Development in Information Technology and Robotics (IDITR 2022). 93–99. https://doi.org/10.1109/IDITR54676.2022.9796495.
Samadi Gharajeh, M., and H. B. Jond. 2022. An Intelligent Approach for Autonomous Mobile Robots Path Planning Based on Adaptive Neuro-Fuzzy Inference System. Ain Shams Engineering Journal. 13(1): 101491. https://doi.org/10.1016/j.asej.2021.05.005.
Sadeghian, P., X. Zhao, A. Golshan, and J. Håkansson. 2022. A Stepwise Methodology for Transport Mode Detection in GPS Tracking Data. Travel Behaviour and Society. 26: 159–67. https://doi.org/10.1016/j.tbs.2021.10.004.
Shan, X., A. Cabani, and H. Chafouk. 2022. Cooperative Localization Based on GPS Correction and EKF in Urban Environment. In Proceedings of the 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET 2022). 1–8. https://doi.org/10.1109/IRASET52964.2022.9738388.
Tian, K., and K. Mirza. 2022. Sensor Fusion for Octagon—An Indoor and Outdoor Autonomous Mobile Robot. In Proceedings of the IEEE International Systems Conference (SysCon 2022). 1–5. https://doi.org/10.1109/SysCon53536.2022.9773827.
Lai, X., S. Tong, and G. Zhu. 2022. Adaptive Fuzzy Neural Network–Aided Progressive Gaussian Approximate Filter for GPS/INS Integration Navigation. Measurement. 200: 111641. https://doi.org/10.1016/j.measurement.2022.111641.
Zhu, G. 2022. An Adaptive Fuzzy Neural Network–Based Progressive Gaussian Approximate Filter with Variable Step Size. Information Technology and Control. 51(1): 86–103. https://doi.org/10.5755/j01.itc.51.1.29776.
Dahmane, B., B. Lejdel, E. Clementini, F. Harrats, S. Nassar, and L. H. Abderrahmane. 2022. Controlling the Degree of Observability in GPS/INS Integration Land-Vehicle Navigation Based on Extended Kalman Filter. Bulletin of Electrical Engineering and Informatics. 11(2): 702–712. https://doi.org/10.11591/eei.v11i2.3695.
Chen, W.-Y., H.-Y. Chang, C.-Y. Wang, and W.-H. Chung. 2022. Cooperative Neighboring Vehicle Positioning Systems Based on Graph Convolutional Network: A Multi-Scenario Transfer Learning Approach. In Proceedings of the IEEE International Conference on Communications (ICC 2022). 3226–3231. https://doi.org/10.1109/ICC45855.2022.9838627.
Yuan, L., H. Chen, Y. Wang, and X. Lian. 2022. Fuse GPS Course Angle with Quaternion to Improve GPS/IMU-Based Velocity Estimation Accuracy. In Proceedings of the 2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE). 1–8. IEEE. https://doi.org/10.1109/ICARCE55724.2022.10046582.
Pagoti, S. K., and S. I. D. Vemuri. 2022. Development and Performance Evaluation of Correntropy Kalman Filter for Improved Accuracy of GPS Position Estimation. International Journal of Intelligent Networks. 3: 1–8. https://doi.org/10.1016/j.ijin.2022.01.002.
Engleman, K., H. Vega, J. Suway, and E. Desai. 2022. Positional Accuracy of Portable GPS Devices during Different Ride Conditions. SAE International Journal of Advances and Current Practices in Mobility. 4(6): 2022-01-0828. https://doi.org/10.4271/2022-01-0828.
Ouda, A. N., and A. Mohamed. 2022. Hybrid Positioning Technique Based Integration of GPS/INS for Autonomous Vehicle Navigation. Advances in Military Technology. 17(2): 357–382. https://doi.org/10.3849/aimt.01498.
Shaiju, M., and S. Sreeja. 2022. Characterization of Cubature Kalman Filter for GPS-Delayed Environments in INS/GPS Integrated Navigation. IFAC-PapersOnLine. 55(22): 207–211. https://doi.org/10.1016/j.ifacol.2023.03.035.
Pagoti, S. K., S. I. D. Vemuri, and G. Laveti. 2022. GPS Receiver Position Augmentation Using Correntropy Kalman Filter in Low Latitude Terrain. International Arab Journal of Information Technology. 19(1): 72–80. https://doi.org/10.34028/iajit/19/1/9.
Lohar, P., S. Khillare, T. Ghodke, and P. R. Asati. 2023. Design and Implementation of Vehicle Tracking System with GPS. International Journal of Research in Applied Science and Engineering Technology. 11(5): 4329–4335. https://doi.org/10.22214/ijraset.2023.52530.
Wen, Z., G. Yang, and Q. Cai. 2023. An Autonomous Smartphone-Embedded Inertial Sensors–Aided Vehicular Navigation Method in Satellite-Denied Areas. Measurement. 215: 112788. https://doi.org/10.1016/j.measurement.2023.112788.
Kheirandish, M., E. A. Yazdi, H. Mohammadi, and M. Mohammadi. 2023. A Fault-Tolerant Sensor Fusion in Mobile Robots Using Multiple-Model Kalman Filters. Robotics and Autonomous Systems. 161: 104343. https://doi.org/10.1016/j.robot.2022.104343.
Zhumu, F., L. Yuxuan, S. Pengju, T. Fazhan, and W. Nan. 2023. A Multisensor High-Precision Location Method in Urban Environment. IEEE Systems Journal. 17(4): 6611–6622. https://doi.org/10.1109/JSYST.2023.3316140.
Zhu, K., C. Deng, F. Zhang, H. Kang, Z. Wen, and G. Guo. 2023. A Multi-Source Fusion Navigation System to Overcome GPS Interruption of Unmanned Ground Vehicles. IEEE Access. 11: 61070–61081. https://doi.org/10.1109/ACCESS.2023.3282219.
Luo, J., Z. Yin, L. Gui, and X. Yang. 2023. Accurate Localization for Indoor and Outdoor Scenario by GPS and UWB Fusion. In Proceedings of the 9th International Conference on Control, Automation and Robotics (ICCAR 2023). 411–416. https://doi.org/10.1109/ICCAR57134.2023.10151723.
Sayeedunnisa, S. F., K. H. Saberi, and M. A. Mohiuddin. 2023. Augmented GPS Navigation: Enhancing the Reliability of Location-Based Services. In Proceedings of the International Conference on Advanced Computing and Communication Technologies (InCACCT 2023). 565–569. https://doi.org/10.1109/InCACCT57535.2023.10141739.
Deng, M. 2023. Robot Navigation Based on Multi-Sensor Fusion. Journal of Physics: Conference Series. 2580(1). https://doi.org/10.1088/1742-6596/2580/1/012020.
Takanose, A., K. Kondo, Y. Hoda, J. Meguro, and K. Takeda. 2023. Localization System for Vehicle Navigation Based on GNSS/IMU Using Time-Series Optimization with Road Gradient Constraint. Journal of Robotics and Mechatronics. 35(2): 387–397. https://doi.org/10.20965/jrm.2023.p0387.
Zhang, Q., D. Liu, H. Liu, Y. Li, and G. Li. 2023. GPS Navigation and Location Method for Outdoor Firefighting Robot Based on Kalman Filter. In Proceedings of the IEEE SmartWorld Conference (SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023). 1–5. https://doi.org/10.1109/SWC57546.2023.10448803.
Pacheco, M. V. O., F. O. Silva, and J. A. Farrell. 2023. GPS-Aided Odometry Navigation for IARs: Comparison Between Loosely and Tightly Coupled Integrations under Restricted Satellite Visibility Conditions. In Proceedings of the Latin American Robotics Symposium, Brazilian Symposium on Robotics, and Workshop on Robotics in Education (LARS/SBR/WRE 2023). 278–83. https://doi.org/10.1109/LARS/SBR/WRE59448.2023.10333060.
Song, J., P. J. Sanchez-Cuevas, A. Richard, and M. Olivares-Mendez. 2023. “GPS-Aided Visual Wheel Odometry. In Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC 2023). 375–82. https://doi.org/10.1109/ITSC57777.2023.10422097.
Welsh, T., S. M. Marks, and A. Pronschinske. 2023. GPS-Denied Vehicle Localization for Augmented Reality Using a Road-Aided Particle Filter and RGB Camera. In Proceedings of the IEEE/ION Position, Location and Navigation Symposium (PLANS 2023). 1363–72. https://doi.org/10.1109/PLANS53410.2023.10140123.
Wang, Z., C. He, and Z. Miao. 2023. Navigation Control of Mobile Robot Based on Fuzzy Neural Network. In Proceedings of the 3rd Asia-Pacific Conference on Communication Technology and Computer Science (ACCTCS 2023). 98–102. https://doi.org/10.1109/ACCTCS58815.2023.00070.
Ma, C. H., Y. K. Dong, S. P. Chen, C. Y. Peng, and G. S. Huang. 2023. Outdoor Positioning Based on ROS LiDAR Navigation Compared with RTK GPS Accuracy. In Proceedings of the International Automatic Control Conference (CACS 2023). 1–5. https://doi.org/10.1109/CACS60074.2023.10325865.
Abdolkarimi, E. S., and M. R. Mosavi. 2023. A Modified Neuro-Fuzzy System for Accuracy Improvement of Low-Cost MEMS-Based INS/GPS Navigation System. Wireless Personal Communications. 129(2): 1369–92. https://doi.org/10.1007/s11277-023-10194-w.
You, B., G. Zhong, C. Chen, J. Li, and E. Ma. 2023. A Simultaneous Localization and Mapping System Using the Iterative Error-State Kalman Filter Judgment Algorithm for Global Navigation Satellite System. Sensors. 23(13). https://doi.org/10.3390/s23136000.
Nguyen-Ngoc, T. T., T. D. Phi, P. Q. Phan-Nguyen, and V. H. Nguyen. 2023. Tightly Coupled GPS/INS/LiDAR Integration for Road Vehicles. In Proceedings of the International Symposium on Electrical and Electronics Engineering (ISEE 2023). 156–60. https://doi.org/10.1109/ISEE59483.2023.10299860.
Wei, X., P. Lang, J. Li, K. Feng, and Y. Zhan. 2024. A Hybrid Optimization Method Based on Extreme Learning Machine–Aided Factor Graph for INS/GPS Information Fusion during GPS Outages. Aerospace Science and Technology. 152: 109326. https://doi.org/10.1016/j.ast.2024.109326.
Hu, M., T. Song, and J. Ye. 2024. A Hybrid Method for INS/GPS Integrated Navigation System Based on the Improved Kalman Filter and Backpropagation Neural Network. In Proceedings of the 8th International Conference on Robotics Control and Automation (ICRCA 2024). 477–84. https://doi.org/10.1109/ICRCA60878.2024.10648989.
Cho, S. H., and S. Choi. 2024. Accurate and Resilient GPS-Only Localization with Velocity Constraints. IEEE Access. 12: 105686–702. https://doi.org/10.1109/ACCESS.2024.3432335.
Liu, Z., J. Liu, X. Xu, and K. Wu. 2024. DeepGPS: Deep Learning–Enhanced GPS Positioning in Urban Canyons. IEEE Transactions on Mobile Computing. 23(1): 376–92. https://doi.org/10.1109/TMC.2022.3208240.
Aher, P. B., M. Bopche, A. Humne, J. Sayse, and A. Ghule. 2024. Autonomous Skateboard with DTMF-Enabled Obstacle Detection System and GPS Navigation. International Research Journal of Modern Engineering, Technology and Science. https://doi.org/10.56726/IRJMETS52120.
Mwitta, C., and G. C. Rains. 2024. The Integration of GPS and Visual Navigation for Autonomous Navigation of an Ackermann-Steering Mobile Robot in Cotton Fields. Frontiers in Robotics and AI. 11. https://doi.org/10.3389/frobt.2024.1359887.
Alaba, S. Y. 2024. GPS–IMU Sensor Fusion for Reliable Autonomous Vehicle Position Estimation. arXiv. http://arxiv.org/abs/2405.08119.
Bhat, S., and A. Kavasseri. 2024. Multi-Source Data Integration for Navigation in GPS-Denied Autonomous Driving Environments. International Journal of Electrical and Electronics Research. 12(3): 863–69. https://doi.org/10.37391/ijeer.120317.
Onyema, U. C., and M. Shafik. 2024. Predictive Machine Learning–Based Error Correction in GPS/IMU Localization to Improve Navigation of Autonomous Vehicles. MATEC Web of Conferences. 401: 12004. https://doi.org/10.1051/matecconf/202440112004.
Wang, Y., R. Sun, Q. Cheng, and W. Y. Ochieng. 2024. Measurement Quality Control–Aided Multisensor System for Improved Vehicle Navigation in Urban Areas. IEEE Transactions on Industrial Electronics. 71(6): 6407–17. https://doi.org/10.1109/TIE.2023.3288188.
Sellak, S. 2024. Monocular Visual Odometry in Mobile Robot Navigation. In Proceedings of the IEEE International Symposium on Signal, Image, Video and Communications (ISIVC 2024). 1–6. https://doi.org/10.1109/ISIVC61350.2024.10577766.
Elsergany, A. M., M. F. Abdel-Hafez, and M. A. Jaradat. 2024. Novel Augmented Quaternion Unscented Kalman Filter for Enhanced Loosely Coupled GPS/INS Integration. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/TCST.2024.3425211.
Hayashi, T., et al. 2024. Development of Autonomous Mobile Field Robot: Accuracy Verification of Self-Localization through Simulation. In Proceedings of the International Conference on Artificial Life and Robotics. 29: 446–49. https://doi.org/10.5954/icarob.2024.os16-2.
Badawy, A. A., M. A. Hassan, A. H. Hassaballa, and Y. Z. Elhalwagy. 2024. Real-Time Integration of GPS with Gyro-Compassing Using Two Cascaded EKFs with FreeRTOS. In Proceedings of the 6th International Conference on Computing and Informatics (ICCI 2024). 307–14. https://doi.org/10.1109/ICCI61671.2024.10485034.
Zhang, M., Y. Luo, and K. A. Neusypin. 2024. Research on Combined GNSS/IMU/Camera Positioning and Navigation in Full Scene. In Proceedings of the International Russian Smart Industry Conference (SmartIndustryCon 2024). 327–32. https://doi.org/10.1109/SmartIndustryCon61328.2024.10516097.
Anwar, M. M., P. Pandey, A. K. Jha, G. Balahia, S. Jaiswal, and R. Shanker. 2024. Streamlining Navigation Using Sensor Fusion for GPS and Augmented Reality. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4486992.
Zhang, L., H. Zhao, J. Chen, L. Li, and X. Liu. 2024. Vehicular Positioning Based on GPS/IMU Data Fusion Aided by V2X Networks. IEEE Sensors Journal. 24(6): 9032–43. https://doi.org/10.1109/JSEN.2024.3355185.
Zhang, S., R. Tu, Z. Gao, D. Zou, S. Wang, and X. Lu. 2024. LEO-Enhanced GNSS/INS Tightly Coupled Integration Based on Factor Graph Optimization in Urban Environment. Remote Sensing. 16(10). https://doi.org/10.3390/rs16101782.
Zhang, L., Y. Lou, W. Song, W. Zhang, and Z. Peng. 2024. Performance Enhancement of PPP/SINS Tightly Coupled Navigation Based on Improved Robust Maximum Correntropy Kalman Filtering. Advances in Space Research. 74(5): 2078–91. https://doi.org/10.1016/j.asr.2024.05.072.
Cheng, B., R. Du, B. Yang, W. Yu, C. Chen, and X. Guan. 2011. An Accurate GPS-Based Localization in Wireless Sensor Networks: A GM-WLS Method. In Proceedings of the International Conference on Parallel Processing Workshops. 33–41. https://doi.org/10.1109/ICPPW.2011.32.
Alshamaa, D., F. Mourad-Chehade, P. Honeine, and A. Chkeir. 2020. An Evidential Framework for Localization of Sensors in Indoor Environments. Sensors. 20(1): 1–17. https://doi.org/10.3390/s20010318.
Drawil, N. M., H. M. Amar, and O. A. Basir. 2013. GPS Localization Accuracy Classification: A Context-Based Approach. IEEE Transactions on Intelligent Transportation Systems. 14(1): 262–73. https://doi.org/10.1109/TITS.2012.2213815.
Kumar, P. Sirish, and V. B. S. S. I. Dutt. 2020. The Global Positioning System: Popular Accuracy Measures. Materials Today: Proceedings. 33: 4797–4801. https://doi.org/10.1016/j.matpr.2020.08.380.
Hashim, R., M. S. Ikhmatiar, M. Surip, M. Karmin, and T. Herawan. 2010. A Mobile GPS Application: Mosque Tracking with Prayer Time Synchronization. Communications in Computer and Information Science. 119: 237–46. https://doi.org/10.1007/978-3-642-17587-9_27.
Blesing, C., J. Finke, S. Hoose, A. Schweigert, and J. Stenzel. 2023. Accuracy Evaluation of a Low-Cost Differential Global Positioning System for Mobile Robotics. In Proceedings of the IEEE Sensors Conference. 1–4. https://doi.org/10.1109/SENSORS56945.2023.10324934.
Abdelhafid, E. F., Y. M. Abdelkader, M. Ahmed, E. H. Doha, E. K. Oumayma, and E. A. Abdellah. 2022. Localization Based on DGPS for Autonomous Robots in Precision Agriculture. In Proceedings of the 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET 2022). 1–4. https://doi.org/10.1109/IRASET52964.2022.9737758.
Specht, C. 2023. Maritime DGPS System Positioning Accuracy as a Function of the HDOP in the Context of Hydrographic Survey Performance. Remote Sensing. 15(1). https://doi.org/10.3390/rs15010010.
Saputra, R. P., and E. Rijanto. 2012. Automatic Guided Vehicles System and Its Coordination Control for Container Terminal Logistics Application. In Proceedings of the International Logistics Seminar and Workshop. http://arxiv.org/abs/2104.08331.
Tang, M. 2012. Evolutionary Placement of Continuously Operating Reference Stations of Network Real-Time Kinematic. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2012). 1–8. https://doi.org/10.1109/CEC.2012.6256527.
Alkan, R. M., S. Erol, V. İlçi, and M. Ozulu. 2020. Comparative Analysis of Real-Time Kinematic and PPP Techniques in Dynamic Environment. Measurement. 163: 107995. https://doi.org/10.1016/j.measurement.2020.107995.
Ismail, A., A. S. A. Rashid, R. Sa’Ari, and A. W. Rasib. 2020. Application of Photogrammetric Technique in Determination of Rock Slope Stability of Quarry. IOP Conference Series: Materials Science and Engineering. 932(1). https://doi.org/10.1088/1757-899X/932/1/012054.
Hasanujjaman, M., M. Z. Chowdhury, and Y. M. Jang. 2023. Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking. Sensors. 23(6). https://doi.org/10.3390/s23063335.
Vasile, I., E. Tudor, I. C. Sburlan, M. A. Gheți, and G. Popa. 2021. Experimental Validation of LiDAR Sensors Used in Vehicular Applications by Using a Mobile Platform for Distance and Speed Measurements. Sensors. 21(23). https://doi.org/10.3390/s21238147.
Lee, D., M. Jung, W. Yang, and A. Kim. 2024. LiDAR Odometry Survey: Recent Advancements and Remaining Challenges. Intelligent Service Robotics. 17(2): 95–118. https://doi.org/10.1007/s11370-024-00515-8.
Yang, S., H. Wang, and X. Liu. 2024. A LiDAR-Inertial Odometry by Fusing Adjacent Frames with Point Selection. In Proceedings of the 36th Chinese Control and Decision Conference, 4972–77. https://doi.org/10.1109/CCDC62350.2024.10587847.
Thakur, A., and P. Rajalakshmi. 2024. LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation. IEEE Open Journal of Instrumentation and Measurement. 3: 1–11. https://doi.org/10.1109/OJIM.2024.3412219.
He, J., H. Huang, S. Zhang, J. Jiao, C. Liu, and M. Liu. 2024. Accurate Prior-Centric Monocular Positioning with Offline LiDAR Fusion. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2024). 11934–40. https://doi.org/10.1109/ICRA57147.2024.10611105.
Bhatta, N. P., and M. G. Priya. 2017. RADAR and Its Applications. International Journal of Computer Technology and Applications. 10(3): 1–9.
Shin, S., M. H. Kim, and S. B. Choi. 2016. Improving Efficiency of Ultrasonic Distance Sensors Using Pulse Interval Modulation. In Proceedings of the IEEE Sensors Conference. 1–3. https://doi.org/10.1109/ICSENS.2016.7808766.
Kanwal, A., Z. Anjum, and W. Muhammad. 2021. Visual Simultaneous Localization and Mapping (vSLAM) of Driverless Car in GPS-Denied Areas. Engineering Proceedings. 12(1). https://doi.org/10.3390/engproc2021012049.
Zhao, H., et al. 2019. Heading Drift Reduction for Foot-Mounted Inertial Navigation System via Multi-Sensor Fusion and Dual-Gait Analysis. IEEE Sensors Journal. 19(19): 8514–21. https://doi.org/10.1109/JSEN.2018.2866802.
Ding, W., J. Wang, C. Rizos, and D. Kinlyside. 2007. Improving Adaptive Kalman Estimation in GPS/INS Integration. Journal of Navigation. 60(3): 517–29. https://doi.org/10.1017/S0373463307004316.
De Giorgi, C., D. De Palma, and G. Parlangeli. 2024. Online Odometry Calibration for Differential Drive Mobile Robots in Low Traction Conditions with Slippage. Robotics. 13(1). https://doi.org/10.3390/robotics13010007.
Suwandi, B., W. S. Pinastiko, and R. Roestam. 2019. OBD-II Sensor Approaches for the IMU and GPS-Based Apron Vehicle Positioning System. In Proceedings of the International Conference on Sustainable Engineering and Creative Computing (ICSECC 2019). 251–54. https://doi.org/10.1109/ICSECC.2019.8907036.
Cahyadi, M. N., T. Asfihani, R. Mardiyanto, and R. Erfianti. 2023. Performance of GPS and IMU Sensor Fusion Using Unscented Kalman Filter for Precise i-Boat Navigation in Infinite Wide Waters. Geodesy and Geodynamics. 14(3): 265–74. https://doi.org/10.1016/j.geog.2022.11.005.
Denewiler, T. 2011. Robot Traffic School: Improving Autonomous Navigation in EOD Robots. University of California, San Diego.
Cheng, J., L. Zhang, Q. Chen, X. Hu, and J. Cai. 2023. Map-Aided Visual–Inertial Fusion Localization Method for Autonomous Driving Vehicles. Measurement. 221: 113432. https://doi.org/10.1016/j.measurement.2023.113432.
Ulrich, J., J. Blaha, A. Alsayed, T. Rouček, F. Arvin, and T. Krajník. 2023. Real-Time Fiducial Marker Localisation System with Full 6-DOF Pose Estimation. ACM SIGAPP Applied Computing Review. 23(1): 20–35. https://doi.org/10.1145/3594264.3594266.
Alatise, M. B., and G. P. Hancke. 2017. Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter. Sensors. 17(10). https://doi.org/10.3390/s17102164.
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