Abstract
Fast and accurate localization for unmanned aerial vehicle (UAV) navigation could avoid hazards when GPS is unavailable. A vision-based global positioning system is developed for UAV by means of satellite images from Google Map as reference. In this system, an ultra-robust and fast feature correspondence algorithm, called Grid-based motion statistics (GMS), is utilized for scene matching. GMS’s performance is comparable to the techniques many orders of magnitude slower, and it maintains high speed as the fast algorithms. A Least Median Square improved GMS algorithm (LMedS-GMS) is developed which is employed to solve the matching failure of the original GMS in challenging scenarios. Moreover, a robust filtering localization approach combining the random sample consensus algorithm (RANSAC) and the proposed LMedS-GMS is designed for locating the position of an UAV. Finally, a vision-based global positioning architecture is proposed using this method with the altitude and direction information from the airborne sensors. Experimental results based on offline data demonstrate that the proposed algorithm is superior to state-of-the-art methods in the accuracy and real-time performance.
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References
Cesetti, A., Frontoni, E., Mancini, A., et al.: A vision-based guidance system for UAV navigation and safe landing using natural landmarks. J. Intell. Rob. Syst. 57(1–4), 233–257 (2010)
Conte, G., Doherty, P.: Vision-based unmanned aerial vehicle navigation using geo-referenced information. In: 2008 IEEE Aerospace Conference, pp. 1–18 (2009)
Amidi, O., Kanade, T., Fujita, K.: A visual odometer for autonomous helicopter flight. Robot. Auton. Syst. 28(2–3), 185–193 (1999)
Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry for ground vehicle applications. J. Field Robot. 23(1), 3–20 (2006)
Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Rob. 31(5), 1147–1163 (2015)
Weiss, S., Scaramuzza, D., Siegwart, R.: Monocular-SLAM-based navigation for autonomous micro helicopters in GPS-denied environments. J. Field Robot. 28(6), 854–874 (2011)
Caballero, F., Merino, L., Ferruz, J., et al.: A visual odometer without 3D reconstruction for aerial vehicles. Applications to building inspection. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4673–4678 (2006)
Caballero, F., Merino, L., Ferruz, J., et al.: Vision-based odometry and SLAM for medium and high altitude flying UAVs. J. Intell. Rob. Syst. 54(1–3), 137–161 (2009)
Conte, G., Doherty, P.: A visual navigation system for uas based on geo-referenced imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 38(1), 101–106 (2011)
Cesetti, A., Frontoni, E., Mancini, A., et al.: A visual global positioning system for unmanned aerial vehicles used in photogrammetric applications. J. Intell. Rob. Syst. 61(1–4), 157–168 (2011)
Ascani, A., Frontoni, E., Mancini, A., et al.: Feature group matching for appearance-based localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3933–3938 (2008)
Shan, M., Wang, F., Lin, F., et al.: Google map aided visual navigation for UAVs in GPS-denied environment. In: IEEE International Conference on Robotics and Biomimetics, pp. 114–119 (2017)
Lindsten, F., Callmer, J., Ohlsson, H., et al.: Geo-referencing for UAV navigation using environmental classification. In: IEEE International Conference on Robotics and Automation, pp. 1420–1425 (2010)
Majdik, A.L., Albers-Schoenberg, Y., Scaramuzza, D.: MAV urban localization from google street view data. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3979–3986 (2013)
Seema, B.S., Hemanth, K., Naidu, V.P.S.: Geo-registration of aerial images using RANSAC algorithm. In: NCTAESD 2014, Vemana Institute of Technology, Bangalore, pp. 1–5 (2014)
Saranya, K.C., Naidu, V.P.S., Singhal, V., et al.: Application of vision based techniques for UAV position estimation. In: 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS), pp. 1–5 (2016)
Mantelli, M., Pittol, D., Neuland, R., et al.: A novel measurement model based on abBRIEF for global localization of a UAV over satellite images. Robot. Auton. Syst. 112(1), 304–319 (2019)
Bian, J.W., Lin, W.Y., Matsushita, Y., et al.: GMS: grid-based motion statistics for fast, ultra-robust feature correspondence. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2828–2837 (2017)
Lin, W.-Y., Cheng, M.-M., Lu, J., Yang, H., Do, M.N., Torr, P.: Bilateral functions for global motion modeling. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 341–356. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_23
Lipman, Y., Yagev, S., Poranne, R., et al.: Feature matching with bounded distortion. ACM Trans. Graph. 33(3), 1–14 (2014)
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Xin, L., He, X., Cui, X., Liu, Z. (2023). Vision-Based Global Positioning System Using Improved GMS Algorithm for a UAV. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_89
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