Tag-based Visual Odometry Estimation for Indoor UAVs Localization (2309.13311v1)
Abstract: The agility and versatility offered by UAV platforms still encounter obstacles for full exploitation in industrial applications due to their indoor usage limitations. A significant challenge in this sense is finding a reliable and cost-effective way to localize aerial vehicles in a GNSS-denied environment. In this paper, we focus on the visual-based positioning paradigm: high accuracy in UAVs position and orientation estimation is achieved by leveraging the potentials offered by a dense and size-heterogenous map of tags. In detail, we propose an efficient visual odometry procedure focusing on hierarchical tags selection, outliers removal, and multi-tag estimation fusion, to facilitate the visual-inertial reconciliation. Experimental results show the validity of the proposed localization architecture as compared to the state of the art.
- J. Kim, S. Kim, C. Ju, and H. I. Son, “Unmanned aerial vehicles in agriculture: A review of perspective of platform, control, and applications,” IEEE Access, vol. 7, pp. 105 100–105 115, 2019.
- H. Shakhatreh, A. H. Sawalmeh, A. Al-Fuqaha, Z. Dou, E. Almaita, I. Khalil, N. S. Othman, A. Khreishah, and M. Guizani, “Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges,” IEEE Access, vol. 7, pp. 48 572–48 634, 2019.
- N. Elmeseiry, N. Alshaer, and T. Ismail, “A detailed survey and future directions of unmanned aerial vehicles (UAVs) with potential applications,” Aerospace, vol. 8, no. 12, p. 363, 2021.
- N. Gyagenda, J. V. Hatilima, H. Roth, and V. Zhmud, “A review of gnss-independent UAV navigation techniques,” Robotics and Autonomous Systems, p. 104069, 2022.
- H. Wen, W. Nie, X. Yang, and M. Zhou, “UAV indoor localization using 3D laser radar,” in IEEE 10th Asia-Pacific Conf. on Antennas and Propagation. IEEE, 2022, pp. 1–2.
- X. W. Leong and H. Hesse, “Vision-based navigation for control of micro aerial vehicles,” in 4th IRC Conf. on Science, Engineering and Technology. Springer, 2019, pp. 413–427.
- Y. Song and L.-T. Hsu, “Tightly coupled integrated navigation system via factor graph for UAV indoor localization,” Aerospace Science and Technology, vol. 108, p. 106370, 2021.
- W. You, F. Li, L. Liao, and M. Huang, “Data fusion of UWB and IMU based on unscented kalman filter for indoor localization of quadrotor UAV,” IEEE Access, vol. 8, pp. 64 971–64 981, 2020.
- C. Sandamini, M. W. P. Maduranga, V. Tilwari, J. Yahaya, F. Qamar, Q. N. Nguyen, and S. R. A. Ibrahim, “A review of indoor positioning systems for UAV localization with machine learning algorithms,” Electronics, vol. 12, no. 7, p. 1533, 2023.
- M. Y. Arafat, M. M. Alam, and S. Moh, “Vision-based navigation techniques for unmanned aerial vehicles: Review and challenges,” Drones, vol. 7, no. 2, p. 89, 2023.
- X. Zhang, J. Jiang, Y. Fang, X. Zhang, and X. Chen, “Enhanced fiducial marker based precise landing for quadrotors,” in IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics. IEEE, 2019, pp. 1353–1358.
- M. Bertoni, S. Michieletto, R. Oboe, and G. Michieletto, “Indoor visual-based localization system for multi-rotor UAVs,” Sensors, vol. 22, no. 15, p. 5798, 2022.
- N. Kayhani, A. Heins, W. Zhao, M. Nahangi, B. McCabe, and A. P. Schoelligb, “Improved tag-based indoor localization of UAVs using extended kalman filter,” in Int. Symposium on Automation and Robotics in Construction, 2019, pp. 21–24.
- R. Hartley, J. Trumpf, Y. Dai, and H. Li, “Rotation averaging,” Int. Journal of Computer Vision, vol. 103, pp. 267–305, 2013.
- F. L. Markley, Y. Cheng, J. L. Crassidis, and Y. Oshman, “Averaging quaternions,” Journal of Guidance, Control, and Dynamics, vol. 30, no. 4, pp. 1193–1197, 2007.