Tag-based Visual Odometry Estimation for Indoor UAVs Localization
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.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.