Intelligent Localization Using Vision and Inertial Sensor Fusion

  • Authors

    • Jacques Arsac Professor of Computer Science, University of Paris, France Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2023PII7K2D

    Published 10-04-2023

  • Intelligent Automation, Vision-Inertial Fusion, Robot Localization, Sensor Fusion, Adaptive Filtering, Mobile Robotics

    Issue

    Vol. 6 No. 2 (2023)

    Section

    Articles

    How to Cite

    [1]
    J. Arsac, “Intelligent Localization Using Vision and Inertial Sensor Fusion”, IJIARE, vol. 6, no. 2, pp. 01–08, Oct. 2023, doi: 10.67228/30715725/IJIARE-2023PII7K2D.

  • Abstract

    Autonomous mobile robots operating in dynamically changing, unstructured environments require high-precision, drift-free localization capabilities to achieve robust operational safety and navigational efficacy. While visual Simultaneous Localization and Mapping (vSLAM) and Inertial Navigation Systems (INS) serve as foundational technologies in intelligent automation, standalone implementations encounter significant vulnerabilities, specifically optical occlusion and cumulative dead-reckoning drift. This paper presents a comprehensive study on an intelligent, optimization-based, tightly-coupled vision-inertial sensor fusion framework designed for robust localization in challenging environments. The proposed system integrates high-frequency inertial measurements from an Inertial Measurement Unit (IMU) with high-fidelity visual landmarks extracted from a monocular camera, utilizing an artificial intelligence-driven adaptive Extended Kalman Filter (EKF) state estimation matrix to dynamically adjust measurement noise weights. By analyzing the structural characteristics of feature tracking alongside high-frequency acceleration profiles, the intelligent layer dampens sensor anomalies caused by aggressive motion or lightning fluctuations. Experimental validations conducted using the EuRoC MAV public benchmark dataset indicate that the proposed intelligent fusion architecture provides superior performance across dynamic trajectories, reducing the Absolute Trajectory Error (ATE) by up to 34% compared to classical loosely-coupled filtering methods while maintaining sub-centimeter positional drift thresholds.

  • References

    [1] Mourikis, A. I., & Roumeliotis, S. I. (2007). A multi-state constraint Kalman filter for vision-aided inertial navigation. Proceedings of the IEEE International Conference on Robotics and Automation, 3565-3572.

    [2] Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., & Furgale, P. (2015). Keyframe-based visual-inertial odometry using nonlinear optimization. The International Journal of Robotics Research, 34(3), 314-334.

    [3] Qin, T., Li, P., & Shen, S. (2018). VINS-Mono: A robust and versatile monetary visual-inertial state estimator. IEEE Transactions on Robotics, 34(4), 1004-1020.

    [4] Burri, M., Bloesch, M., Achtelik, M. K., & Siegwart, R. (2016). The EuRoC micro aerial vehicle datasets. The International Journal of Robotics Research, 35(10), 1157-1163.

    [5] Geneva, P., Eckenhoff, K., Yang, Y., & Huang, G. (2020). OpenVINS: A research platform for visual-inertial estimation. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 4666–4672.

    [6] Campos, C., Elvira, R., Rodríguez, J. J. G., Montiel, J. M. M., & Tardós, J. D. (2021). ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap SLAM. IEEE Transactions on Robotics, 37(6), 1874–1890.

    [7] Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., & Furgale, P. (2015). Keyframe-based visual-inertial odometry using nonlinear optimization. The International Journal of Robotics Research, 34(3), 314–334.

    [8] Mourikis, A. I., & Roumeliotis, S. I. (2007). A multi-state constraint Kalman filter for vision-aided inertial navigation. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 3565–3572.

    [9] Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. MIT Press.

    [10] Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to autonomous mobile robots (2nd ed.). MIT Press.

    [11] Aslan, M. F., Durdu, A., Yusefi, A., & Yilmaz, A. (2022). HVIOnet: A deep learning based hybrid visual–inertial odometry approach for unmanned aerial system position estimation. Neural Networks, 155, 461–474.

  • Downloads