, 2026
ADAPTIVE TERRAIN-REFERENCED VISION SYSTEM FOR AUTONOMOUS MULTICOPTER NAVIGATION
This paper proposes an onboard computer vision system with adaptive selection of recognition methods for autonomous multicopter navigation in Global Navigation Satellite Systems (GNSS)-denied environments. The approach represents terrain images as one-dimensional structural signals obtained through preprocessing, segmentation, grayscale conversion, and linearization. Recognition is performed using spectral, wavelet, and dynamic signal matching methods. An adaptive mechanism selects the most appropriate recognition method based on adequacy criteria describing the uniformity, symmetry, convergence, and sensitivity of the recognition process. The selected or combined methods are used for terrain recognition, checkpoint localization, trajectory reconstruction, and flight correction within a unified closed-loop navigation framework. The proposed architecture enables reliable autonomous navigation under spatial distortions, changing observation conditions, and limited onboard computational resources. Experimental results demonstrate that adaptive selection and weighted combination of recognition methods improve the robustness and accuracy of terrain image recognition for navigation in GNSS-denied conditions (pp.45-55).
- Bay, H., Tuytelaars, T., & Van Gool, L. (2006). SURF: Speeded up robust features. In Proceedings of the European Conference on Computer Vision (pp. 404–417). https://doi.org/10.1007/11744023_32
- Bryson, M., & Sukkarieh, S. (2008). Observability analysis and active control for airborne SLAM. IEEE Transactions on Aerospace and Electronic Systems, 44(1), 261–280. https://doi.org/10.1109/TAES.2008.4516992
- Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332. https://doi.org/10.1109/TRO.2016.2624754
- Groves, P. D. (2013). Principles of GNSS, inertial, and multisensor integrated navigation systems (2nd ed.). Artech House.
- Keogh, E., & Pazzani, M. (2001). Derivative dynamic time warping. In Proceedings of the SIAM International Conference on Data Mining (pp. 1–11).
- Kim, J., & Sukkarieh, S. (2007). Real-time implementation of airborne inertial-SLAM. Robotics and Autonomous Systems, 55(1), 62–71. https://doi.org/10.1016/j.robot.2006.08.009
- Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
- Mallat, S. (2009). A wavelet tour of signal processing (3rd ed.). Academic Press.
- Oppenheim, A. V., & Schafer, R. W. (2010). Discrete-time signal processing (3rd ed.). Prentice Hall.
- Rzayev, R., Aliyev, V., & Kerimov, A. (2025a). Evaluation of the accuracy of recognition methods on different examples of color image interpretation. In C. Kahraman et al. (Eds.), Intelligent and Fuzzy Systems. INFUS 2025. Lecture Notes in Networks and Systems (Vol. 1529). Springer, Cham. https://doi.org/10.1007/978-3-031-97992-7_75
- Rzayev, R., Kerimov, A., & Aliyev, V. (2025b). Application of machine vision technology for multicopter flight control under active operation of counter-UAV systems. In K. Arai (Ed.), Intelligent Computing. CompCom 2025. Lecture Notes in Networks and Systems (Vol. 1425). Springer, Cham. https://doi.org/10.1007/978-3-031-92608-2_7
- Rzayev, R. R., Kerimov, A. B., Garibli, U. G., & Salmanov, F. M. (2024). Criteria for assessing the adequacy of image recognition methods and their verification using examples of artificial series of signals. Problems of Information Society, 15(1), 10–17. https://doi.org/10.25045/jpis.v15.i1.02
- Rzayev, R. R. (2012). Neuro-fuzzy modeling of economic behavior (in Russian). Lambert Academic Publishing.
- Rzayev, R. R. (2016). Analytical decision support in organizational systems (in Russian). Palmerium Academic Publishing.
- Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43–49. https://doi.org/10.1109/TASSP.1978.1163055
- Scaramuzza, D., & Fraundorfer, F. (2011). Visual odometry. IEEE Robotics & Automation Magazine, 18(4), 80–92. https://doi.org/10.1109/MRA.2011.943233
- Weiss, S., Achtelik, M. W., Lynen, S., Chli, M., & Siegwart, R. (2012). Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments. In Proceedings of the IEEE International Conference on Robotics and Automation (pp. 957–964). https://doi.org/10.1109/ICRA.2012.6225143
