Integration of Kalman State Prediction and Outer-Loop Velocity PID Control for Robust Autonomous Monocular Unmanned Aerial Vehicle Landing

Precise autonomous landing of unmanned aerial vehicles in global positioning system (GPS)-denied environments remains a challenging control problem owing to image-plane measurement noise, sensor latency, and aerodynamic disturbances. This study proposes a prediction-informed landing framework that integrates a discrete-time Kalman filter tuned for ArUco marker motion in the monocular image plane with an outer-loop velocity proportional-integral-derivative controller that references the predicted state rather than the raw visual measurement. The filter operates on a state vector comprising the image-plane marker features and their temporal derivatives, supplying temporally consistent, noise-attenuated estimates throughout the landing sequence. Performance is evaluated through comparative numerical simulation and real-flight experiments under uncontrolled outdoor disturbances and benchmarked against a conventional raw-measurement pipeline. The proposed integration reduces the closed-loop steady-state positional error by 86.7% and reduces the total three-dimensional root-mean-square error relative to the baseline, with improvements of 20.8% and 21.3% in the longitudinal and lateral channels, respectively. The closed-loop settling time is preserved across all conditions, confirming that the gains primarily arise from the enhanced state estimation rather than from the control law. Real-flight results demonstrate operation under uncontrolled outdoor disturbances, with horizontal velocity tracking root-mean-square errors of 0.162 and 0.190 m/s, without requiring an additional landing sensor beyond the downward-facing monocular camera.

ArUco marker detection, Kalman state prediction, Monocular vision, Proportional-integral-derivative control, Unmanned aerial vehicle

Adel, A., Alani, N. H. S., Thompson Whiteside, S., & Jan, T. (2024). Who is watching whom? Military and civilian drone: Vision intelligence investigation and recommendations. IEEE Access, 12, 177236–177276. https://doi.org/10.1109/ACCESS.2024.3505034

Aikins, G., Jagtap, S., & Nguyen, K. D. (2024). A robust strategy for UAV autonomous landing on a moving platform under partial observability. Drones, 8(6). https://doi.org/10.3390/drones8060232

Alotaibi, E. T., Alqefari, S. S., & Koubaa, A. (2019). LSAR: Multi-UAV collaboration for search and rescue missions. IEEE Access, 7, 55817–55832. https://doi.org/10.1109/ACCESS.2019.2912306

Alqudsi, Y., & Makaraci, M. (2025). UAV swarms: Research, challenges, and future directions. Journal of Engineering and Applied Science, 72(1), 1–24. https://doi.org/10.1186/s44147-025-00582-3

Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138–146. https://doi.org/10.1890/120150

Byun, W., Huh, S., Jang, H., Yu, S., Lim, S., Lee, S., & Nam, W. (2025). Vector field-based robust quadrotor landing on a moving ground platform. Aerospace, 12(7), 1–19. https://doi.org/10.3390/aerospace12070590

Chang, Y., Cheng, Y., Manzoor, U., & Murray, J. (2023). A review of UAV autonomous navigation in GPS-denied environments. Robotics and Autonomous Systems, 170, 104533. https://doi.org/10.1016/j.robot.2023.104533

Chaumette, F., & Hutchinson, S. (2006). Visual servo control. I. Basic approaches. IEEE Robotics and Automation Magazine, 13(4), 82–90. https://doi.org/10.1109/MRA.2006.250573

Criollo, L., Mena-Arciniega, C., & Xing, S. (2024). Classification, military applications, and opportunities of unmanned aerial vehicles. Aviation, 28(2), 115–127. https://doi.org/10.3846/aviation.2024.21672

Deng, L., Hao, X., Mao, Z., Yan, Y., Sun, J., & Zhang, A. (2018). A subband radiometric calibration method for UAV-based multispectral remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8), 2869–2880. https://doi.org/10.1109/JSTARS.2018.2842466

Eltayeb, A., Rahmat, M. F., Basri, M. A. M., Eltoum, M. A. M., & Mahmoud, M. S. (2022). Integral adaptive sliding mode control for quadcopter UAV under variable payload and disturbance. IEEE Access, 10, 94754–94764. https://doi.org/10.1109/ACCESS.2022.3203058

Fu, Y., An, W., Su, X., & Song, B. (2025). Real-time wind estimation for fixed-wing UAVs. Drones, 9(8), 1–17. https://doi.org/10.3390/drones9080563

Gaigalas, J., Perkauskas, L., Gricius, H., Kanapickas, T., & Kriš?i?nas, A. (2025). A framework for autonomous UAV navigation based on monocular depth estimation. Drones, 9(4). https://doi.org/10.3390/drones9040236

Gamagedara, K., Lee, T., & Snyder, M. (2024). Delayed Kalman filter for vision-based autonomous flight in ocean environments. Control Engineering Practice. https://doi.org/10.1016/j.conengprac.2023.105791

Ghamari, M., Rangel, P., Mehrubeoglu, M., Tewolde, G. S., & Sherratt, R. S. (2022). Unmanned aerial vehicle communications for civil applications: A review. IEEE Access, 10, 102492–102531. https://doi.org/10.1109/ACCESS.2022.3208571

Guo, Z., Wang, J., Zheng, X., Zhou, Y., & Zhang, J. (2025). A visual guidance and control method for autonomous landing of a quadrotor UAV on a small USV. Drones, 9(5). https://doi.org/10.3390/drones9050364

Heryanto, M. A., & Kusumoputro, B. (2021). Attitude and altitude control of quadcopter maneuvers using neural network–based direct inverse control. International Journal of Technology, 12(4), 843–853. https://doi.org/10.14716/ijtech.v12i4.3928

Heselden, J. R., Paparas, D., Stevenson, R. L., & Das, G. P. (2025). Enhanced STag marker system: Materials and methods for flexible robot localisation. Machines, 13(1), 1–36. https://doi.org/10.3390/machines13010002

Jarraya, I., Al-Batati, A., Kadri, M. B., Abdelkader, M., Ammar, A., Boulila, W., & Koubaa, A. (2025). GNSS-denied unmanned aerial vehicle navigation: Analyzing computational complexity, sensor fusion, and localization methodologies. Satellite Navigation, 6(1). https://doi.org/10.1186/s43020-025-00162-z

Jones, S., & Hauert, S. (2023). Frappe: Fast fiducial detection on low cost hardware. Journal of Real-Time Image Processing, 20(6). https://doi.org/10.1007/s11554-023-01373-w

Jung, S. (2024). Precision landing of unmanned aerial vehicle under wind disturbance using derivative sliding mode nonlinear disturbance observer-based control method. Aerospace, 11(4). https://doi.org/10.3390/aerospace11040265

Jurado-Rodriguez, D., Munoz-Salinas, R., Garrido-Jurado, S., & Medina-Carnicer, R. (2021). Design, detection, and tracking of customized fiducial markers. IEEE Access, 9, 140066–140078. https://doi.org/10.1109/ACCESS.2021.3118049

Kalaitzakis, M., Cain, B., Carroll, S., Ambrosi, A., Whitehead, C., & Vitzilaios, N. (2021). Fiducial markers for pose estimation. Journal of Intelligent & Robotic Systems, 101(4). https://doi.org/10.1007/s10846-020-01307-9

Laghari, A. A., Jumani, A. K., Laghari, R. A., & Nawaz, H. (2023). Unmanned aerial vehicles: A review. Cognitive Robotics, 3, 8–22. https://doi.org/10.1016/j.cogr.2022.12.004

Lee, J. C., Chen, C. C., Shen, C. T., & Lai, Y. C. (2022). Landmark-based scale estimation and correction of visual inertial odometry for VTOL UAVs in a GPS-denied environment. Sensors, 22(24). https://doi.org/10.3390/s22249654

Li, Y., Peng, B., He, L., Fan, K., & Tong, L. (2019). Road segmentation of unmanned aerial vehicle remote sensing images using adversarial network with multiscale context aggregation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2279–2287. https://doi.org/10.1109/JSTARS.2019.2909478

Lopez-Sanchez, I., & Moreno-Valenzuela, J. (2023). PID control of quadrotor UAVs: A survey. Annual Reviews in Control, 56, 100900. https://doi.org/10.1016/j.arcontrol.2023.100900

Mohd Daud, S. M. S., Mohd Yusof, M. Y. P., Heo, C. C., Khoo, L. S., Chainchel Singh, M. K., Mahmood, M. S., & Nawawi, H. (2022). Applications of drone in disaster management: A scoping review. Science and Justice, 62(1), 30–42. https://doi.org/10.1016/j.scijus.2021.11.002

Mohsan, S. A. H., Othman, N. Q. H., Li, Y., Alsharif, M. H., & Khan, M. A. (2023). Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends. Intelligent Service Robotics, 16(1), 109–137. https://doi.org/10.1007/s11370-022-00452-4

Murugan, D., Garg, A., & Singh, D. (2017). Development of an adaptive approach for precision agriculture monitoring with drone and satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12), 5322–5328. https://doi.org/10.1109/JSTARS.2017.2746185

Nabavi, Y., Asadi, D., & Ahmadi, K. (2022). Automatic landing control of a multi-rotor UAV using a monocular camera. Journal of Intelligent and Robotic Systems: Theory and Applications, 105(3). https://doi.org/10.1007/s10846-022-01655-8

Nenni, M. E., Di Pasquale, V., Miranda, S., & Riemma, S. (2020). Development of a drone-supported emergency medical service. International Journal of Technology, 11(4), 656–666. https://doi.org/10.14716/ijtech.v11i4.3951

Park, Y., Park, C., Song, W., Lee, C., Kwon, J., Park, J., Noh, G., & Lee, D. (2024). Fiducial marker-based autonomous landing using image filter and Kalman filter. International Journal of Aeronautical and Space Sciences, 25(1), 190–199. https://doi.org/10.1007/s42405-023-00635-y

Paucar, C., Morales, L., Pinto, K., Sánchez, M., Rodríguez, R., Gutierrez, M., & Palacios, L. (2018). Use of drones for surveillance and reconnaissance of military areas. Smart Innovation, Systems and Technologies, 94, 119–132. https://doi.org/10.1007/978-3-319-78605-6 10

Qolbiyah, N. S., El Rumy, M. A., Purnama, I., & Faizal, F. (2024). Real-time peatland fire detection using infrared cameras and SVM algorithm. ICSMech 2024 – 1st International Conference on Smart Mechatronics: Transformative Innovations in Smart Mechatronics: Bridging AI, Robotics, and IoT for a Sustainable Future, 198–201. https://doi.org/10.1109/ICSMech62936.2024.10812237

Rahman, M. A., Azad, S., Asyhari, A. T., Bhuiyan, M. Z. A., & Anwar, K. (2018). Collab-SAR: A collaborative avalanche search-and-rescue missions exploiting hostile alpine networks. IEEE Access, 6, 42094–42107. https://doi.org/10.1109/ACCESS.2018.2848366

Santos, M. C., Bartlett, B., Schneider, V. E., Brádaigh, F. O., Blanck, B., Santos, P. C., Trslic, P., Riordan, J., & Dooly, G. (2024). Cooperative unmanned aerial and surface vehicles for extended coverage in maritime environments. IEEE Access, 12, 9206–9219. https://doi.org/10.1109/ACCESS.2024.3353046

Schempp, C., & Schulz, S. (2022). High-precision absolute pose sensing for parallel mechanisms. Sensors, 22(5), 1–33. https://doi.org/10.3390/s22051995

Semerikov, S. O., Nechypurenko, P. P., Vakaliuk, T. A., Mintii, I. S., & Kolhatin, A. O. (2025). Vision-based autonomous UAV landing: A comprehensive review of technologies, techniques, and applications. Journal of Intelligent and Robotic Systems: Theory and Applications. https://doi.org/10.1007/s10846-025-02314-4

Simon, D. (2006). Optimal state estimation: Kalman, H infinity, and nonlinear approaches. John Wiley & Sons. https://doi.org/10.1002/0470045345

Sonugür, G. (2023). A review of quadrotor UAV: Control and SLAM methodologies ranging from conventional to innovative approaches. Robotics and Autonomous Systems, 161, 104342. https://doi.org/10.1016/j.robot.2022.104342

Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information (Switzerland), 10(11). https://doi.org/10.3390/info10110349

Xin, L., Tang, Z., Gai, W., & Liu, H. (2022). Vision-based autonomous landing for the UAV: A review. Aerospace, 9(11). https://doi.org/10.3390/aerospace9110634

Zhang, Y., Wu, Z., & Wei, T. (2024). Aerodynamic disturbance estimation in quadrotor landing on moving platform via noise reduction extended disturbance observer. IEEE Sensors Journal, 24(22), 37566–37574. https://doi.org/10.1109/JSEN.2024.3472025

Zhou, X., Chen, L., Sun, C., Jia, W., Yi, N., & Sun, W. (2025). Highly accurate attitude estimation of unmanned aerial vehicle payloads using low-cost MEMS. Micromachines, 16(6), 1–18. https://doi.org/10.3390/mi16060632