Research on Multi-Sensor Fusion Technology, Application Scenarios, and Future Prospects Review

Main Article Content

Feiyang Zhao
School of Electronic and Optical Engineering, Nanjing University of Posts and Telccommunications, Nanjing 210023, China

DOI: https://doi.org/10.70267/cai.26v3n3.3442

Keywords

multi-sensor fusion, fusion algorithm, robot, intelligent recognition, healthcare

Abstract

To better acquire information and understand actual situations, multi-sensor technology has been widely applied in various industries. It represents a significant advancement over single sensors and is currently a core method for perceiving complex environments. However, issues such as inconvenient information collection, improper data processing, and insufficient environmental adaptability persist. This paper systematically elaborates on the technical background of multi-sensor fusion technology, its core algorithms including Kalman filtering and Bayesian estimation, as well as its applications in robotics, intelligent monitoring, and healthcare. It also discusses existing problems and corresponding solutions, and provides an outlook on the future application of sensors in fields like quantum technology and bionics, as well as potential emerging industries.

Abstract 0 | PDF Downloads 0

References

  • [1] Xu, Q., Gao, Z., Lv, J., & Yang, C. (2023). Tightly coupled integration of BDS-3 B2b RTK, IMU, odometer, and dual-antenna attitude. IEEE Internet of Things Journal, 10(7), 6415–6427. https://doi.org/10.1109/JIOT.2022.3227019
  • [2] Wen, S., Othman, K. M., Rad, A. B., & Zhang, Y. (2014). Indoor SLAM using laser and camera with closed-loop controller for NAO humanoid robot. Abstract and Applied Analysis, 2014, Article 513175. https://doi.org/10.1155/2014/513175
  • [3] Kewley, D. J. (1993). A model for evaluating data fusion systems. In Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers (Vol. 1, pp. 273–277). IEEE. https://doi.org/10.1109/ACSSC.1993.342516
  • [4] Tian, J., Chen, J., Dou, L., & Zhang, Y. (2002). The research of test and evaluation for multisensor data fusion systems. In Proceedings of the 4th World Congress on Intelligent Control and Automation (Vol. 3, pp. 2104–2108). IEEE. https://doi.org/10.1109/WCICA.2002.1021456
  • [5] Yan, H. C., Huang, X. H., & Wang, M. (2005). Multi-sensor data fusion technology and its application. Journal of Transducer Technology, 24(10), 6–9. https://doi.org/10.13873/j.1000-97872005.10.001
  • [6] Li, Z. X. (2025). Application of multi-sensor information fusion and decision control in autonomous navigation of blind-guiding robots. Heilongjiang Science, 16(6), 19–22, 27.
  • [7] Shao, M. Z., He, T., Zhu, Y. P., et al. (2023). Research on mobile robot navigation and positioning based on multi-sensor information fusion. Machine Tool & Hydraulics, 51(5), 8–13.
  • [8] Zhang, J., & Singh, S. (2017). Low-drift and real-time lidar odometry and mapping. Autonomous Robots, 41(2), 401–416. https://doi.org/10.1007/s10514-016-9548-2
  • [9] Lynen, S., Achtelik, M. W., Weiss, S., Chli, M., & Siegwart, R. (2013). A robust and modular multi-sensor fusion approach applied to MAV navigation. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3923–3929). IEEE. https://doi.org/10.1109/IROS.2013.6696917
  • [10] Bai, X., Yun, Z., Xuan, D., Jia, W., & Zhao, W. (2010). Pattern mutation in wireless sensor deployment. In Proceedings of the 29th IEEE International Conference on Computer Communications (INFOCOM 2010) (pp. 1–9). IEEE. https://doi.org/10.1109/INFCOM.2010.5462076
  • [11] Fan, X. M. (2025). Research on three-dimensional WSN coverage optimization based on an improved gray wolf optimization algorithm. Sensors and Microsystems, 44(8), 148–153. https://doi.org/10.13873/J.1000-9787(2025)08-0148-06
  • [12] Rong, W., Zhang, Y., Wang, S., et al. (2024). Wireless sensor network coverage optimization based on improved genetic algorithm. Sensors and Microsystems, 43(6), 141–144. https://doi.org/10.13873/J.1000-9787(2024)06-0141-04
  • [13] Zhu, X., Yi, J., Cheng, J., & He, L. (2020). Adapted error map based mobile robot UWB indoor positioning. IEEE Transactions on Instrumentation and Measurement, 69(9), 6336–6350. https://doi.org/10.1109/TIM.2020.2967114
  • [14] Fan, J. C., Yu, Z. G., Xie, D., et al. (2025). Novel FPGA architecture and circuit design technology outlook. Integrated Circuits and Embedded Systems, 25(6), 14–28. https://doi.org/10.20193/j.ices2097-4191.2025.0016
  • [15] Zhu, Y. H. (2025). Application and data analysis of intelligent sensors in medical equipment maintenance. Digital Technology & Application, 43(12), 102–104.

Article Sidebar

PDF
Published
May 7, 2026
Issue
Vol. 3 No. 3 (2026)
Section
Research Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Zhao, F. (2026). Research on Multi-Sensor Fusion Technology, Application Scenarios, and Future Prospects Review. Computers and Artificial Intelligence, 3(3), 34-42. https://doi.org/10.70267/cai.26v3n3.3442