Adaptive?Size Fiducial?Marker Detection with Dual?Stage Kalman Filtering for Precision UAV Landing

Authors

  • K. P. Ayodele Department of Electronics & Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
  • P. T. Okewunmi Department of Electronics & Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
  • S. O. Akinola Department of Electronics & Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
  • A. A. Ogunseye Department of Electronics & Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
  • M. I. Eghrudje Department of Electronics & Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
  • F. B. Offiong Department of Electrical and Electronic Engineering, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, United Kingdom
  • S. P. Olayiwola Department of Electronics & Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
  • A. R. Lawal Department of Computer Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
  • A. F. Omowonuola Department of Remote Sensing and Geoscience Information System, Federal University of Technology Akure, Nigeria
  • A. A. Ishola Department of Mechatronics Engineering. Federal University of Agriculture, Abeokuta, Nigeria

DOI:

https://doi.org/10.63746/njtd.v22i4.3078

Keywords:

Adaptive fiducial marker, Dual‑stage Kalman filter, E‑ink display, Precision landing, UAV

Abstract

Precision UAV landing systems face significant challenges with traditional fixed-marker approaches, particularly under varying environmental conditions and integration scenarios. This paper presents a novel approach combining dynamically-sized fiducial markers with dual-stage Kalman filtering to address these limitations. The methodology utilizes a 13.3-inch e-ink display showing ArUco markers that dynamically scale from 16.7 cm to 2.8 cm during descent, actively reducing marker size while accounting for e-ink display refresh constraints through predictive state estimation. A vision system processes frames at 30Hz through an optimized detection pipeline, while the dual-stage Kalman filter handles both vehicle state estimation and parameter adaptation, managing display transition dynamics. The system's universal protocol framework enables compatibility with both integrated and unknown UAV platforms through a dual-mode detection approach, combining passive visual detection with active altitude signalling. Experimental validation using a DJI F450 platform with downward-facing camera demonstrates the system achieves mean landing errors of 1.95 cm under wind conditions up to 15 km/h, representing a 59% improvement over fixed-marker approaches. The e-ink display enables reliable marker detection up to 14.2m altitude under direct sunlight conditions, outperforming LCD displays which become undetectable in bright conditions. Pixel-wise RMSE improved from 150.3 to 140.5 (x-axis) and 170.8 to 151.4 (y-axis) over four sequential landings, demonstrating robust operation across varying integration scenarios.

References

Arents, R., Groeneweg, J., Mulder, M., & Van Paassen, M. (2009). Predictive landing guidance in synthetic vision displays. In AIAA Guidance, Navigation, and Control Conference (p. 5984). https://doi.org/10.2514/6.2009-5984

Bastiaens, S., Mommerency, J., Deprez, K., Joseph, W., & Plets, D. (2021). Received signal strength visible light positioning-based precision drone landing system. In 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–8). IEEE. https://doi.org/10.1109/IPIN51102.2021.9634055

Borowczyk, A., Nguyen, D. T., Phu-Van Nguyen, A., Nguyen, D. Q., Saussié, D., & Le Ny, J. (2017). Autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle. IFAC-Papers OnLine, 50(1), 10488–10494. https://doi.org/10.1016/j.ifacol.2017.08.2032

Cabrera-Ponce, A. A., & Martinez-Carranza, J. (2017, October). A vision-based approach for autonomous landing. In 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) (pp. 126–131). IEEE. https://doi.org/10.1109/RED-UAS.2017.8116073

Claro, R. M., Silva, D. B., & Pinto, A. M. (2023). ArTuga: A novel multimodal fiducial marker for aerial robotics. Robotics and Autonomous Systems, 163, 104398. https://doi.org/10.1016/j.robot.2023.104398

Ferrão, J. M. L. (2023). A multimodal vision-based sensor fusion approach for precise landing of an UAV (Master’s thesis). Universidade do Porto, Portugal.

Nguyen, P. H., Kim, K. W., Lee, Y. W., & Park, K. R. (2017). Remote marker-based tracking for UAV landing using visible-light camera sensor. Sensors, 17(9), 1987. https://doi.org/10.3390/s17091987

Park, Y., Song, W., Lee, C., Kwon, J., & Park, J. (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/s42405-023-00605-2

Pieczy?ski, D., Ptak, B., Kraft, M., Piechocki, M., & Aszkowski, P. (2024). A fast, lightweight deep learning vision pipeline for autonomous UAV landing support with added robustness. Engineering Applications of Artificial Intelligence, 131, 107864. https://doi.org/10.1016/j.engappai.2023.107864

Ulrich, J., Alsayed, A., Arvin, F., & Krajník, T. (2022). Towards fast fiducial marker with full 6 DoF pose estimation. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (pp. 723–730). https://doi.org/10.1145/3477314.3507636

Wang, Z., She, H., & Si, W. (2017). Autonomous landing of multi-rotors UAV with monocular gimbaled camera on moving vehicle. In 2017 13th IEEE International Conference on Control & Automation (ICCA) (pp. 408–412). IEEE. https://doi.org/10.1109/ICCA.2017.8284507

Wu, Y., Niu, X., Du, J., Chang, L., Tang, H., & Zhang, H. (2019). Artificial marker and MEMS IMU-based pose estimation method to meet multirotor UAV landing requirements. Sensors, 19(24), 5428. https://doi.org/10.3390/s19245428

Wubben, J., Fabra, F., Calafate, C. T., Krzeszowski, T., Marquez-Barja, J. M., Cano, J. C., & Manzoni, P. (2019). Accurate landing of unmanned aerial vehicles using ground pattern recognition. Electronics, 8(12), 1532. https://doi.org/10.3390/electronics8121532

Yasentsev, D., Shevgunov, T., Efimov, E., & Tatarskiy, B. (2021). Using ground-based passive reflectors for improving UAV landing. Drones, 5(4), 137. https://doi.org/10.3390/drones5040137

Published

2025-09-29

Similar Articles

1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.