Position Estimation of Unmanned Aerial Vehicles in Contested Environments using Dense Matching Networks
Resumo
This paper presents an approach to predicting the trajectory and the position of unmanned aerial vehicles (UAVs) in contested global navigation satellite system (GNSS) environments. Our approach utilizes dense matching networks to analyze and predict patterns in UAV movement and then effectively recovers temporal trajectory information and accurately predicts UAV position changes using images and altitude data. This problem was part of the KDDBR 2022 Kaggle Competition, where we obtained a Root Mean Squared Error (RMSE) of 0.29603, which made it the runner-up solution. This could have significant implications for the future of UAV navigation, potentially leading to safer and more efficient operations in various applications.
Palavras-chave:
Unmanned Aerial Vehicles, Trajectory Prediction, Dense Matching Network
Referências
Anderson, M., Brink, K., and Willis, A. (2019). Real-time visual odometry covariance estimation for unmanned air vehicle navigation. Journal of Guidance, Control, and Dynamics, 42:1–17.
Barmpounakis, E. N., Vlahogianni, E. I., and Golias, J. C. (2016). Unmanned aerial aircraft systems for transportation engineering: Current practice and future challenges. International Journal of Transportation Science and Technology, 5(3):111–122.
Dilshad, N., Ullah, A., Kim, J., and Seo, J. (2023). Locateuav: Unmanned aerial vehicle location estimation via contextual analysis in an iot environment. IEEE Internet of Things Journal, 10(5):4021–4033.
Erdelj, M., Natalizio, E., Chowdhury, K. R., and Akyildiz, I. F. (2017). Help from the sky: Leveraging uavs for disaster management. IEEE Pervasive Computing, 16(1):24–32.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 3149–3157, Red Hook, NY, USA. Curran Associates Inc.
Kruber, F., Morales, E. S., Chakraborty, S., and Botsch, M. (2020). Vehicle position estimation with aerial imagery from unmanned aerial vehicles. arXiv.org.
Li, Z. and Snavely, N. (2018). Megadepth: Learning single-view depth prediction from internet photos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2041–2050.
Liu, X., Wang, H., Fu, D., Yu, Q., Guo, P., Lei, Z., and Shang, Y. (2015). An areabased position and attitude estimation for unmanned aerial vehicle navigation. Science China. Technological sciences, 58(5):916–926.
Mostafa, S. A., Mustapha, A., Shamsudin, A. U., Ahmad, A., Ahmad, M. S., and Shamini Gunasekaran, S. (2018). A real-time autonomous flight navigation trajectory assessment for unmanned aerial vehicles. In 2018 International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR), pages 1–6. IEEE.
Rovira-Sugranes, A., Razi, A., Afghah, F., and Chakareski, J. (2022). A review of aienabled routing protocols for uav networks: Trends, challenges, and future outlook. Ad Hoc Networks, 130:102790.
Truong, P., Danelljan, M., Van Gool, L., and Timofte, R. (2021). Learning accurate dense correspondences and when to trust them. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5714–5724.
Barmpounakis, E. N., Vlahogianni, E. I., and Golias, J. C. (2016). Unmanned aerial aircraft systems for transportation engineering: Current practice and future challenges. International Journal of Transportation Science and Technology, 5(3):111–122.
Dilshad, N., Ullah, A., Kim, J., and Seo, J. (2023). Locateuav: Unmanned aerial vehicle location estimation via contextual analysis in an iot environment. IEEE Internet of Things Journal, 10(5):4021–4033.
Erdelj, M., Natalizio, E., Chowdhury, K. R., and Akyildiz, I. F. (2017). Help from the sky: Leveraging uavs for disaster management. IEEE Pervasive Computing, 16(1):24–32.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 3149–3157, Red Hook, NY, USA. Curran Associates Inc.
Kruber, F., Morales, E. S., Chakraborty, S., and Botsch, M. (2020). Vehicle position estimation with aerial imagery from unmanned aerial vehicles. arXiv.org.
Li, Z. and Snavely, N. (2018). Megadepth: Learning single-view depth prediction from internet photos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2041–2050.
Liu, X., Wang, H., Fu, D., Yu, Q., Guo, P., Lei, Z., and Shang, Y. (2015). An areabased position and attitude estimation for unmanned aerial vehicle navigation. Science China. Technological sciences, 58(5):916–926.
Mostafa, S. A., Mustapha, A., Shamsudin, A. U., Ahmad, A., Ahmad, M. S., and Shamini Gunasekaran, S. (2018). A real-time autonomous flight navigation trajectory assessment for unmanned aerial vehicles. In 2018 International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR), pages 1–6. IEEE.
Rovira-Sugranes, A., Razi, A., Afghah, F., and Chakareski, J. (2022). A review of aienabled routing protocols for uav networks: Trends, challenges, and future outlook. Ad Hoc Networks, 130:102790.
Truong, P., Danelljan, M., Van Gool, L., and Timofte, R. (2021). Learning accurate dense correspondences and when to trust them. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5714–5724.
Publicado
25/09/2023
Como Citar
AFONSO, Bruno Klaus de Aquino; DE OLIVEIRA, Willian Dihanster Gomes; LAMOSA, Jéssica Domingues; BERTON, Lilian.
Position Estimation of Unmanned Aerial Vehicles in Contested Environments using Dense Matching Networks. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 584-595.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234283.
