Abstract
Currently, an unmanned aerial vehicle (UAV) utilizes global navigation satellite systems (GNSS) in conjunction with other modalities for localization purposes. Nevertheless, this approach faces robustness issues when GNSS signals become unavailable or sensors malfunction. Clearly, the robustness of the system increases considerably when multiple UAV agents are employed to perform collaborative positioning. In this work, an online distributed solution is proposed for relative localization, which incorporates multiple UAVs together with Signals of Opportunity (SOPs) as well as inertial, visual, and optical flow measurements. The proposed localization system includes relative self-localization of each UAV agent, as well as a reliable distributed relative positioning system (DRPS) for each UAV based on the relative positions from other UAV agents in its vicinity. The latter positioning strategy is required in case the relative self-localization fails, mainly due to such problems as inertial measurement unit (IMU) accumulated error drift, camera sensor errors, or SOP shortfalls due to multipath or antenna obstruction. Extensive field experiments validate the proposed technique and demonstrate increased localization accuracy and robustness when compared to the benchmark approach that does not include cooperation between UAVs.
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References
Maaref, M., Kassas, Z.M.: Ground vehicle navigation in GNSS-challenged environments using signals of opportunity and a closed-loop map-matching approach. IEEE Trans. Intell. Transport. Syst. 21, 2723–2738 (2019)
Coluccia, A., Ricciato, F., Ricci, G.: Positioning based on signals of opportunity. IEEE Commun. Lett. 18, 356–359 (2014)
Morales, J., Kassas, Z.M.: Event-based communication strategy for collaborative navigation with signals of opportunity. In: Proc. Asilomar Conf. on Signals, Systems, and Computers (2018)
Maaref, M., Khalife, J., Kassas, Z.M.: Lane-level localization and mapping in GNSS-challenged environments by fusing lidar data and cellular pseudoranges. IEEE Trans. Intell. Veh. 4, 73–89 (2018)
Angelino, C.V., Baraniello, V.R., Cicala, L.: UAV position and attitude estimation using IMU, GNSS and camera. In: Proc. Int. Conf. on Inf. Fusion (2012)
Simkovits, H., Weiss, A.J., Amar, A.: Navigation by inertial device and signals of opportunity. Signal Process. 131, 280–287 (2017)
Shamaei, K., Khalife, J., Kassas, Z.M.: Exploiting LTE signals for navigation: Theory to implementation. IEEE Trans. Wireless Commun. 17, 2173–2189 (2018)
Kassas, Z.Z.M., Khalife, J., Shamaei, K., Morales, J.: I hear therefore I know where I am: Compensating for GNSS limitations with cellular signals. IEEE Signal Process. Mag. 34, 111–124 (2017)
Michel, A.H.: Counter-drone systems. Center for the Study of the Drone at Bard College. https://dronecenter.bard.edu/files/2019/12/CSD-CUAS-2nd-Edition-Web.pdf (2019). Accessed 15 January 2023
Bresson, G., Alsayed, Z., Yu, L., Glaser, S.: Simultaneous localization and mapping: A survey of current trends in autonomous driving. IEEE Trans. Intell. Veh. 2, 194–220 (2017)
Kapoor, R., Ramasamy, S., Gardi, A., Sabatini, R.: UAV navigation using signals of opportunity in urban environments: A review. Energy Procedia 110, 377–383 (2017)
Cooper, A.J., Redman, C.A., Stoneham, D.M., Gonzalez, L.F., Etse, V.K.: A dynamic navigation model for unmanned aircraft systems and an application to autonomous front-on environmental sensing and photography using low-cost sensor systems. Sensors 15, 21537–21553 (2015)
Morales, J.J., Kassas, Z.M.: Distributed signals of opportunity aided inertial navigation with intermittent communication. In: Proc. Int. Techn. Mtg. Satellite Div. Inst. of Navigat. (2018)
Raquet, J.F., Miller, M.M.: Issues and approaches for navigation using signals of opportunity. In: Proc. National Technical Meeting of The Inst. of Navigat. (2007)
Raquet, J.F.: Navigation using pseudolites, beacons, and signals of opportunity. In: NATO STO Lecture Series SET-197, Navigat. Sensors and Syst. in GNSS Degraded and Denied Environm. (2013)
Zwirello, L., Li, X., Zwick, T., Ascher, C., Werling, S., Trommer, G.F.: Sensor data fusion in UWB-supported inertial navigation systems for indoor navigation. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2013)
Zingg, S., Scaramuzza, D., Weiss, S., Siegwart, R.: MAV navigation through indoor corridors using optical flow. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2010)
Shen, C., Bai, Z., Cao, H., Xu, K., Wang, C., Zhang, H., Wang, D., Tang, J., Liu, J.: Optical flow sensor/INS/magnetometer integrated navigation system for MAV in GPS-denied environment. J. Sensors 2016, 1–11 (2016)
Schmuck P., Chli, M.: Multi-UAV collaborative monocular SLAM. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2017)
Karrer, M., Agarwal, M.. Kamel, M., Siegwart, R., Chli, M.: Collaborative 6DoF relative pose estimation for two UAVs with overlapping fields of view. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2018)
Liu, R., Yuen, C., Do, T-N., Jiao, D., Liu, X., Tan, U-X.: Cooperative relative positioning of mobile users by fusing IMU inertial and UWB ranging information. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2017)
Piasco, N., Marzat, J., Sanfourche, M.: Collaborative localization and formation flying using distributed stereo-vision. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2016)
Karrer, M., Chli, M.: Towards globally consistent visual-inertial collaborative SLAM. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2018)
Souli, N., Kolios, P., Ellinas, G.: Relative positioning of autonomous systems using signals of opportunity. In: Proc. IEEE Veh. Technol. Conf. (VTC2020-Spring) (2020)
Souli, N., Kolios, P., Ellinas, G.: Online relative positioning of autonomous vehicles using signals of opportunity. IEEE Trans. Intell. Veh. 7, 873–885 (2022)
Xu, Y., Ou, Y., Xu, T.: SLAM of robot based on the fusion of vision and LIDAR. In: Proc. IEEE Int. Conf. on Cyborg and Bionic Systems (CBS) (2018)
Meronen, L., Wilkinson, W.J., Solin, A.: Movement tracking by optical flow assisted inertial navigation. In: Proc. IEEE Int. Conf. on Inf. Fusion (2020)
Song, Y., Guan, M., Tay, W.P., Law, C.L, Wen, C.: UWB/LiDAR fusion for cooperative range-only SLAM. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2019)
Moon, S., Youn, W.: A novel movable UWB localization system using UAVs. IEEE Access 10, 41303–41312 (2022)
Queralta, J.P., Martínez Almansa, C., Schiano, F., Floreano, D., Westerlund, T.: UWB-based system for UAV localization in GNSS-denied environments: Characterization and dataset. In: Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) (2020)
Bailey, T., Bryson, M., Mu, H., Vial, J., McCalman, L., Durrant-Whyte, H.: Decentralised cooperative localisation for heterogeneous teams of mobile robots. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2011)
Souli, N., Makrigiorgis, R., Kolios, P., Ellinas, G.: Cooperative relative positioning using signals of opportunity and inertial and visual modalities. In: Proc. IEEE Veh. Technol. Conf. (VTC2021-Spring) (2021)
Powell, W., Ryzhov, I.: Optimal Learning. Wiley, Hoboken, NJ (2012)
Frazier, P., Powell, W., Dayanik, S.: The knowledge-gradient policy for correlated normal beliefs. INF. J. Comput. 21, 599–613 (2009)
Ryzhov, I., Powell, W.B., Frazier, P.I.: The knowledge gradient algorithm for a general class of online learning problems. Oper. Res. 60, 180–195 (2012)
Souli, N., Kolios, P., Ellinas, G.: Adaptive frequency band selection for accurate and fast positioning utilizing SOPs. In: Proc. IEEE International Conference on Unmanned Aircraft System (ICUAS) (2022)
Li, G., Geng, E., Ye, Z., Xu, Y., Lin, J., Pang, Y.: Indoor positioning algorithm based on the improved RSSI distance model. Sensors 18, 1–15 (2018)
Ge, B., Han, J., Zhao, B.: Improved RSSI positioning algorithm for coal mine underground locomotive. J. Electr. Comp. Eng. 2015, 1–8 (2015)
Tomic, S., Beko, M., Dinis, R., Bernardo, L.: On target localization using combined RSS and AoA measurements. Sensors 18, 1–25 (2018)
International Telecommunication Union: Technical and operating parameters and spectrum use for shortrange radio communication devices. ITU-R SM.2153-8. https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-SM.2153-9-2022-PDF-E.pdf (2021). Accessed 15 January 2023
Federal Communications Commission: Study of digital television field strength standards and testing procedures. Report To Congress, ET Docket No. 05-182. https://transition.fcc.gov/oet/info/documents/reports/SHVERA/SHVERA-FCC-05-199.pdf (2005). Accessed 15 January 2023
International Telecommunication Union: Impact of audio signal processing and compression techniques on terrestrial FM sound broadcasting emissions at VHF. ITU-R BS.2213-4. https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-BS.2213-4-2017-PDF-E.pdf (2017). Accessed 15 January 2023
Abhayawardhana, V.S., Wassell, I.J., Crosby, D., Sellars, M.P., Brown, M.G.: Comparison of empirical propagation path loss models for fixed wireless access systems. In: Proc. IEEE Veh. Techn. Conf. (2005)
Ismail, M.: An RSSI-based wireless sensor node localisation using trilateration and multilateration methods for outdoor environment. arXiv:1912.07801 [eess.SP] (2019)
Wang, Y., Yang, X., Zhao, Y., Liu, Y., Cuthbert, L.: Bluetooth positioning using RSSI and triangulation methods. In: Proc. IEEE Consumer Commun. Netw. Conf. (2013)
Silva, H.: Experimental study on RSS based indoor positioning algorithms. In: Yang, G.C., Ao, S.I., Gelman, L. (eds.) Transactions on Engineering Technologies, pp. 451–466. Springer, Dordrecht (2015)
Lee, H.: A novel procedure for assessing the accuracy of hyperbolic multilateration systems. IEEE Trans. Aerosp. Electron. Syst. AES-11, 2–15 (1975)
Brown, R., Hwang, P.: Introduction to Random Signals and Applied Kalman Filtering with MatLab Exercises. Wiley, New York (2011)
Sazdovski, V., Kolemishevska-Gugulovska, T., Stankovski, M.: Kalman filter implementation for unmanned aerial vehicles navigation. IFAC Proc. Volumes 38, 12–17 (2005)
Wickert, M., Siddappa, C.: Exploring the extended Kalman filter for GPS positioning using simulated user and satellite track data. In: Proc. Python in Science Conf. (2018)
Maaloul, B., Taleb-Ahmed, A., Niar, S., Harb, N., Valderrama, C.: Adaptive video-based algorithm for accident detection on highways. In: Proc. IEEE Int. Symp. on Industrial Embedded Systems (2017)
Sigut, J., Castro, M., Arnay, R., Sigut, M.: OpenCV basics: A mobile application to support the teaching of computer vision concepts. IEEE Trans. Educ. 63, 328–335 (2020)
Valenzuela, A.Q., Reyes, J.: Basic spatial resolution metrics for satellite imagers. IEEE Sensors J. 19, 4914–4922 (2019)
Bochkovskiy, A., Wang, C-Y., Liao, H-Y.M.: YOLOv4: Optimal speed and accuracy of object detection. arXiv:2004.10934 [cs.CV] (2020)
Makrigiorgis, R., Kolios, P., Timotheou, S., Theocharides, T., Panayiotou, C.G.: Extracting the fundamental diagram from aerial footage. In: Proc. IEEE Veh. Technol. Conf. (VTC2020-Spring) (2020)
Martoyo, I., Setiasabda, P., Kanalebe, H.Y., Uranus, H.P., Pardede, M.: Software defined radio for education: Spectrum analyzer, FM receiver/transmitter and GSM sniffer with HackRF One. In: Proc. Borneo Int. Conf. on Applied Math. and Eng. (2018)
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: An open-source robot operating system. In: Proc. ICRA Workshop on Open Source Software (2009)
Zhu, N., Marais, J., Bétaille, D., Berbineau, M.: GNSS position integrity in urban environments: A review of literature. IEEE Trans. Intell. Transp. Syst. 19, 2762–2778 (2018)
Zidan, J., Adegoke, E.I., Kampert, E., Birrell, S.A., Ford, C.R., Higgins, M.D.: GNSS vulnerabilities and existing solutions: A review of the literature. IEEE Access 9, 153960–153976 (2021)
Ge, Q., Shao, T., Duan, Z., Wen, C.: Performance analysis of the Kalman filter with mismatched noise covariances. IEEE Trans. Autom. Contr. 61, 4014–4019 (2016)
Saito, A., Kizawa, S., Kobayashi, Y., Miyawaki, K.: Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output. ROBOMECH J. 7, 1–11 (2020)
Morales, J., Kassas, Z.M.: Information fusion strategies for collaborative radio SLAM. In: Proc. IEEE/ION Position, Location and Navigation Symposium (PLANS) (2018)
Ferrigno, L., Miele, G., Milano, F., Pingerna, V., Cerro, G., Laracca, M.: A UWB-based localization system: analysis of the effect of anchor positions and robustness enhancement in indoor environments. In: Proc. IEEE Int. Instrumentation and Measurement Technology Conf. (I2MTC) (2021)
Shi, Q., Zhao, S., Cui, X., Lu, M., Jia, M.: Anchor self-localization algorithm based on UWB ranging and inertial measurements. Tsinghua Sci. Technol. 24, 728–737 (2019)
Kolakowski, J., Consoli, A., Djaja-Josko, V., Ayadi, J., Morrigia, L., Piazza, F., UWB localization in EIGER indoor/outdoor positioning system. In: Proc. IEEE Int. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (2015)
Li, Y., Maorong, J., Qiang, L., Guili, Y., Kai, D.: Research on the IR-UWB ranging algorithm in outdoor near-ground environment. In: Proc. Int. Conf. on Sensor Networks and Signal Processing (SNSP) (2018)
Wang, W., Bai, P., Liang, X., Zhang, J., He, L.: Performance analysis for TDOA localization using UAVs with flight disturbances. In: Proc. Int. Conf. on Inf. Fusion (2017)
Wang, Y., Wu, Y., Shen, Y.: Cooperative tracking by multi-agent systems using signals of opportunity. IEEE Trans. Coms. 68, 93–105 (2020)
Teck, T. Y., Chitre, M., Hover, F. S.: Collaborative bathymetry-based localization of a team of autonomous underwater vehicles. In: Proc. IEEE International Conference on Robotics and Automation (ICRA) (2014)
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This work has been supported by the European Union’s Horizon 2020 research grant agreement 739551 (KIOS CoE - TEAMING) and from the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy
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Open access funding provided by the Cyprus Libraries Consortium (CLC). This work was supported by the European Union’s Horizon 2020 research and innovation program under grant agreements No 101017258 (SESAME) and No 739551 (KIOS CoE - TEAMING) and from the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.
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All authors contributed to the study’s conception and design in an equal manner. Material preparation, analysis, and implementation were performed by Nicolas Souli.
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Souli, N., Kolios, P. & Ellinas, G. Online Distributed Relative Positioning Utilizing Multiple Cooperative Autonomous Agents. J Intell Robot Syst 109, 87 (2023). https://doi.org/10.1007/s10846-023-01992-2
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DOI: https://doi.org/10.1007/s10846-023-01992-2