radioelectronicsИзвестия высших учебных заведений России. РадиоэлектроникаJournal of the Russian Universities. Radioelectronics1993-89852658-4794Saint Petersburg Electrotechnical University10.32603/1993-8985-2024-27-4-6-18radioelectronics-908Research ArticleРАДИОЛОКАЦИЯ И РАДИОНАВИГАЦИЯRADAR AND NAVIGATIONСравнительное исследование навигационных систем для автономных беспилотных летательных аппаратов в помещенииComparative Review of Navigation Systems for Indoor Autonomous Unmanned Aerial VehiclesБоронахинА. М.BoronakhinA. M.

Боронахин Александр Михайлович – доктор технических наук (2013), профессор (2020), профессор кафедры лазерных измерительных и навигационных систем, декан факультета информационно-измерительных и биотехнических систем,

д. 5 Ф, ул. Профессора Попова,  Санкт-Петербург, 197022.

Alexander M. Boronakhin – Dr Sci. (Eng.) (2013), Professor (2020), Professor of the Department of Laser Measuring and Navigation Systems, Dean of the Faculty of Information Measuring and Biotechnical Systems,

5 F, Professor Popov St., St Petersburg 197022.

AMBoronahin@etu.ruНгуенКуок ХаньNguyenQuoc Khanh

Нгуен Куок Хань – магистр по направлению "Приборостроение" (2020), аспирант,

236, Хоанг Куок Вьет, Ко Нхуэ, Бак Ты Лиэм, Ханой.

Nguyen Quoc Khanh – Master of Science in Instrumentation Engineering (2020), PhD student

236, Hoang Quoc Viet, Co Nhue, Bac Tu Liem, Hanoi.

nguyenquockhanh183@gmail.comhttps://orcid.org/0000-0003-4330-8542НгуенЧонг ИенNguyenTrong Yen

Нгуен Чонг Иен – кандидат технических наук по направлению "Электроника, фотоника, приборостроение и связь"(2023), сотрудник отдела "Система бортового управления",

18, Хоанг Куок Вьет, Каузяй, Ханой.

Nguyen Trong Yen – PhD in Electronics, Photonics, Instrumentation and Communications (2023), member of the On-Board Control System Department,

18, Hoang Quoc Viet, Cau Giay, Hanoi.

trongyen@lqdtu.edu.vnСанкт-Петербургский государственный электротехнический университет "ЛЭТИ" им. В. И. Ульянова (Ленина)РоссияSaint Petersburg Electrotechnical UniversityRussian FederationГосударственный технический университет им. Ле Куй ДонаВьетнамLe Quy Don Technical UniversityViet NamАкадемия наук и технологийВьетнамVietnam Academy of Science and TechnologyViet Nam202427092024274618Copyright © Боронахин А.М., Нгуен К.Х., Нгуен Ч.И., 20242024Боронахин А.М., Нгуен К.Х., Нгуен Ч.И.Boronakhin A.M., Nguyen Q.K., Nguyen T.Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://re.eltech.ru/jour/article/view/908Введение

Введение. Беспилотные летательные аппараты (БПЛА) являются активно развивающейся сферой в последние годы. Во всех областях применения БПЛА особое значение уделяется точности позиционирования. Спутниковая система навигации (GPS) является оптимальным методом позиционирования для наружной среды, однако для внутренней среды ослабление сигнала GPS становится серьезным препятствием при определении местоположения БПЛА. Проведено множество исследований, посвященных разработке различных технологий позиционирования в помещении, отвечающих критериям компактности и малой массы, подходящих для малогабаритных летательных аппаратов, включая оптический поток, инерциальную навигационную систему, ультразвук и т. д. В настоящее время имеется немного обзоров технологий позиционирования в помещениях для автономных БПЛА, основанных на поиске информации по соответствующим статьям и на сравнении датчиков. Недостатками этих обзоров является неполнота оценки по основным критериям и неконкретность рассмотрения принципов их работы. С этой целью в данной статье представлен обзор современных технологий позиционирования в помещении, их принципов работы и оценка по разным критериям: точности, рабочему диапазону, стоимости. Дается оценка перспективной технологии на основе машинного зрения.

Цель работы

Цель работы. Классификация современных технологий навигации в помещении для БПЛА, а также оценка рассматриваемых технологии по разных критериям.

Материалы и методы

Материалы и методы. Классификация методов внутреннего позиционирования БПЛА по типу сигнала, используемого для подключения, и способности обработки информации без внешних сигналов. Оценка методов по критериям: точность, рабочий диапазон, стоимость, а также по их преимуществам и недостаткам. Результаты. Представлена таблица классификации и оценки технологий навигации БПЛА в помещении, проведено обзорное представление этой области.

Заключение

Заключение. Проведен обзор систем навигации БПЛА во внутренней среде. Рассмотрена технология на основе машинного зрения как перспективная и многообещающая в данной области.

Introduction

Introduction. In recent years, unmanned aerial vehicles (UAVs) have been a rapidly advancing field. In all areas of UAV application, positioning accuracy is of particular importance. For outdoor environments, satellite navigation systems (such as GPS) are always the method of choice. However, for indoor environments, GPS signal weakening becomes a serious obstacle for determining the UAV location. A number of studies have been carried out to develop various indoor positioning technologies that meet the criteria of compactness and light weight, thus suitable for small aircrafts, including optical flows, inertial measurement systems, ultrasound, etc. However, there is a lack of comparative studies reviewing indoor positioning technologies for autonomous UAVs. The existing reviews fail to provide a comprehensive assessment of such technologies and their operational principles according to the main criteria. In this connection, this paper aims to review modern indoor positioning technologies and their operational principles, conducting evaluation according to such criteria as accuracy, operating range, cost. The assessment of promising machine vision-based technologies is carried out.

Aim

Aim. To classify modern indoor navigation technologies for UAVs; to assess the technologies under consideration according to various criteria.

Materials and methods

Materials and methods. The current technologies for UAV indoor positioning were classified by the signal type used for connection and their capability to process information without external signals. The technologies were assessed according to the following criteria: accuracy, operating range, cost, as well as their advantages and disadvantages.

Results

Results. А classification and evaluation table of UAV indoor positioning technologies is proposed; a review of the current developments in the field is given.

Conclusion

Conclusion. A review of UAV indoor positioning technologies has been carried out. In addition, the prospects of machine vision-based technologies are outlined.

беспилотный летательный аппарат (БПЛА)позиционирование в помещенииоценка характеристиктехнологииоснованные на техническом зренииunmanned aerial vehicle (UAV)indoor positioningperformance assessmentvision-based technologiesReferencesKanellakis C., Nikolakopoulos G. Survey on Computer Vision for UAVs: Current Developments and Trends // J. of Intell Robot Syst. 2017. Vol. 87. P. 141– 168. doi: 10.1007/s10846-017-0483-zKanellakis C., Nikolakopoulos G. Survey on Computer Vision for UAVs: Current Developments and Trends. J. of Intell Robot Syst. 2017, vol. 87, pp. 141– 168. doi: 10.1007/s10846-017-0483-zChao H., Gu Y., Napolitano M. A survey of optical flow techniques for UAV navigation applications // Intern. Conf. on Unmanned Aircraft Systems (ICUAS), Atlanta, USA, 28–31 May 2013. IEEE, 2013. P. 710–716. doi: 10.1109/ICUAS.2013.6564752Chao H., Gu Y., Napolitano M. A Survey of Optical Flow Techniques for UAV Navigation Applications. Intern. Conf. on Unmanned Aircraft Systems (ICUAS), Atlanta, USA, 28–31 May 2013. IEEE, 2013, pp. 710–716. doi: 10.1109/ICUAS.2013.6564752A novel distributed architecture for UAV indoor navigation / L. Yuntian, M. Scanavino, E. Capello, F. Dabbene, G. Guglieri, A. Vilardi // Transportation Research Procedia. 2018. Vol. 35. P. 13–22. doi: 10.1016/j.trpro.2018.12.003Yuntian L., Scanavino M., Capello E., Dabbene F., Guglieri G., Vilardi A. A Novel Distributed Architecture for UAV Indoor Navigation. Transportation Research Procedia. 2018, vol. 35, pp. 13–22. doi: 10.1016/j.trpro.2018.12.003Review of UAV positioning in indoor environments and new proposal based on US measurements / M. C. Pérez, D. Gualda, J. Vicente-Ranera, J. M. Villadangos, J. Ureña // Intern. Conf. on Indoor Positioning and Indoor Navigation, Italy, 10 March 2019. CEUR: Ljubljana, Slovenia, 2019. P. 1–8.Pérez M. C., Gualda D., Vicente-Ranera J., Villadangos J. M., Ureña J. Review of UAV Positioning in Indoor Environments and New Proposal Based on US Measurements. Intern. Conf. on Indoor Positioning and Indoor Navigation, Italy, 10 March 2019. CEUR, Ljubljana, Slovenia, 2019, pp. 1–8.Muhammad A. Comparative Study of Indoor Navigation Systems for Autonomous Flight // Telecommunication Computing Electronics and Control. 2018. Vol. 16, № 1. P. 118–128. doi: 10.12928/telkomnika.v16i1.6814Muhammad A. Comparative Study of Indoor Navigation Systems for Autonomous Flight. Telecommunication Computing Electronics and Control. 2018, vol. 16, no. 1, pp. 118–128. doi: 10.12928/telkomnika.v16i1.6814Roth S., Black M. J. On the spatial statistics of optical flow // Intern. J. of Computer Vision. 2007. Vol. 74, № 1. P. 33–50. doi: 10.1109/ICCV.2005.180Roth S., Black M. J. On the Spatial Statistics of Optical Flow. Intern. J. of Computer Vision. 2007, vol. 74, no. 1, pp. 33–50. doi: 10.1109/ICCV.2005.180Black M. J., Anandan P. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields // Computer Vision and Image Understanding. 1996. Vol. 63, № 1. P. 75–104. doi: 10.1006/cviu.1996.0006Black M. J., Anandan P. The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields. Computer Vision and Image Understanding. 1996, vol. 63, no. 1, pp. 75–104. doi: 10.1006/cviu.1996.0006Lucas B. D., Kanade T. An iterative image registration technique with an application to stereo vision // Proc. of Imaging Understanding Workshop. 1981. P. 121–130.Lucas B. D., Kanade T. An Iterative Image Registration Technique with an Application to Stereo Vision. Proc. of Imaging Understanding Workshop. 1981, pp. 121–130.Horn B., Schunck B. Determining optical flow // Artificial Intelligence. 1981. Vol. 17. P. 185–203. doi: 10.1016/0004-3702(81)90024-2Horn B., Schunck B. Determining Optical Flow. Artificial Intelligence. 1981, vol. 17, pp. 185–203. doi: 10.1016/0004-3702(81)90024-2Srinivasan M. V. An image interpolation technique for the computation of optical flow and egomotion // Biological Cybernetics. 1994. Vol. 71. P. 401–415. doi: 10.1007/BF00198917Srinivasan M. V. An Image Interpolation Technique for the Computation of Optical Flow and Egomotion. Biological Cybernetics. 1994, vol. 71, pp. 401–415. doi: 10.1007/BF00198917Kendoul F., Fantoni I., Nonami K. Optic flowbased vision system for autonomous 3d localization and control of small aerial vehicles // Robotics and Autonomous Systems. 2009. Vol. 57, № 6. P. 591–602. doi: 10.1016/j.robot.2009.02.001Kendoul F., Fantoni I., Nonami K. Optic FlowBased Vision System for Autonomous 3d Localization and Control of Small Aerial Vehicles. Robotics and Autonomous Systems. 2009, vol. 57, no. 6, pp. 591–602. doi: 10.1016/j.robot.2009.02.001Lowe D. G. Distinctive image features from scale-invariant key-points // Intern. J. of Computer Vision. 2004. Vol. 2, № 60. P. 91–110. doi: 10.1023/B%3AVISI.0000029664.99615.94Lowe D. G. Distinctive Image Features from Scale-Invariant Key-Points. Intern. J. of Computer Vision. 2004, vol. 2, no. 60, pp. 91–110. doi: 10.1023/B%3AVISI.0000029664.99615.94The Use of Optical Flow for UAV Motion Estimation in Indoor Environment / Z. Yu, W. Tingting, C. Zhihao, W. Yingxun, Y. Zhenxing // IEEE Chinese Guidance, Navigation and Control Conference, Nanjing, 12–14 Aug. 2016. IEEE, 2016. P. 785–790. doi: 10.1109/CGNCC.2016.7828885Yu Z., Tingting W., Zhihao C., Yingxun W., Zhenxing Y. The Use of Optical Flow for UAV Motion Estimation in Indoor Environment. IEEE Chinese Guidance, Navigation and Control Conf., Nanjing, 12– 14 Aug. 2016. IEEE, 2016. P. 785–790. doi: 10.1109/CGNCC.2016.7828885Fausto F., Ju-Hyeon H. Visual Inertial Navigation for a Small UAV Using Sparse and Dense Optical Flow // Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS), Cranfield, UK, 25–27 Nov. 2019. IEEE, 2019. P. 206–212. doi: 10.1109/REDUAS47371.2019.8999672Fausto F., Ju-Hyeon H. Visual Inertial Navigation for a Small UAV Using Sparse and Dense Optical Flow. Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS), Cranfield, UK, 25–27 Nov. 2019. IEEE, 2019, pp. 206– 212. doi: 10.1109/REDUAS47371.2019.8999672Micro unmanned aerial vehicle visual servoing for cooperative indoor exploration / P. Rudol, M. Wzorek, G. Conte, P. Doherty// Aerospace Conf., Sky, USA, 01–08 March 2008. IEEE, 2008. P. 1–10. doi: 10.1109/AERO.2008.4526558Rudol P., Wzorek M., Conte G., Doherty P. Micro Unmanned Aerial Vehicle Visual Servoing for Cooperative Indoor Exploration. Aerospace Conf., Sky, USA, 01–08 March 2008. IEEE, 2008, pp. 1–10. doi: 10.1109/AERO.2008.45265583D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras / J. A. Paredes, F. J. Álvarez, T. Aguilera, J. M. Villadangos // Sensors. 2017. Vol. 18, № 1. P. 89. doi: 10.3390/s18010089Paredes J. A., Álvarez F. J., Aguilera T., Villadangos J. M. 3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras. Sensors. 2017, vol. 18, no. 1, p. 89. doi: 10.3390/s180100893D Position Estimation of an UAV in Indoor Environments using an Ultrasonic Local Positioning System / D. Gualda, J. Ureña, M. C. Pérez, H. Posso, S. Bachiller, R. Nieto // 9th Intern. Conf. on Indoor Positioning and Indoor Navigation, Nantes, France, 24– 27 Sept. 2018. IFSTTAR, 2018. P. 212808.Gualda D., Ureña J., Pérez M. C., Posso H., Bachiller S., Nieto R. 3D Position Estimation of an UAV in Indoor Environments using an Ultrasonic Local Positioning System. 9th Intern. Conf. on Indoor Positioning and Indoor Navigation, Nantes, France, 24– 27 Sept. 2018. IFSTTAR, 2018, p. 212808.Daly D., Melia T., Baldwin G. Concrete Embedded RFID for Way‐Point Positioning // Intern. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland, 15–17 Sept. 2010. IEEE, 2010. P. 1–10.Daly D., Melia T., Baldwin G. Concrete Embedded RFID for Way‐Point Positioning. Intern. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland, 15–17 Sept. 2010. IEEE, 2010, pp. 1–10.Tiemann J., Wietfeld C. Scalable and precise multi-UAV indoor navigation using TDOA-based UWB localization // Intern. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan, 18–21 Sept. 2017. IEEE, 2017. P. 1–7. doi: 10.1109/IPIN.2017.8115937Tiemann J., Wietfeld C. Scalable and Precise Multi-UAV Indoor Navigation Using TDOA-Based UWB Localization. Intern. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan, 18–21 Sept. 2017. IEEE, 2017, pp. 1–7. doi: 10.1109/IPIN.2017.8115937Multi-modal mapping and localization of unmanned aerial robots based on ultra-wideband and RGB-D sensing / F. J. Perez-Grau, F. Caballero, L. Merino, A. Viguria // IEEE/RSJ Intern. Conf. on Intelligent Robots and Systems (IROS), Vancouver, Canada, 24–28 Sept. 2017. IEEE, 2017. P. 3495–3502. doi: 10.1109/IROS.2017.8206191Perez-Grau F. J., Caballero F., Merino L., Viguria A. Multi-Modal Mapping and Localization of Unmanned Aerial Robots Based on Ultra-Wideband and RGB-D Sensing. IEEE/RSJ Intern. Conf. on Intelligent Robots and Systems (IROS), Vancouver, Canada, 24–28 Sept. 2017. IEEE, 2017, pp. 3495–3502. doi: 10.1109/IROS.2017.8206191The interrogation footprint of RFID-UAV: electromagnetic modelling and experimentations / G. Casati, M. Longhi, D. Latini, F. Carbone, S. Amendola, F. Frate, G. Schiavon, G. Marrocco // IEEE J. of Radio Frequency Identification. 2017. Vol. 1, iss. 2. P. 155–162. doi: 10.1109/JRFID.2017.2765619Casati G., Longhi M., Latini D., Carbone F., Amendola S., Frate F., Schiavon G., Marrocco G. The Interrogation Footprint of RFID-UAV: Electromagnetic Modelling and Experimentations. IEEE J. of Radio Frequency Identification. 2017, vol. 1, iss. 2, pp. 155– 162. doi: 10.1109/JRFID.2017.2765619Alajami A., Moreno G., Pous R. Design of a UAV for Autonomous RFID-Based Dynamic Inventories Using Stigmergy for Mapless Indoor Environments // Drones. 2022. Vol. 6, iss. 8. P. 208. doi: 10.3390/drones6080208Alajami A., Moreno G., Pous R. Design of a UAV for Autonomous RFID-Based Dynamic Inventories Using Stigmergy for Mapless Indoor Environments. Drones. 2022, vol. 6, iss. 8, p. 208. doi: 10.3390/drones6080208Wi‐Fi Positioning: System Considerations and Device Calibration / T. Vaupel, J. Seitz, F. Kiefer, S. Haimerl, J. Thielecke // Intern. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland, 15–17 Sept. 2010. IEEE, 2010. P. 1–7. doi: 10.1109/IPIN.2010.5646207Vaupel T., Seitz J., Kiefer F., Haimerl S., Thielecke J. Wi‐Fi Positioning: System Considerations and Device Calibration. Intern. Conf. on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland, 15–17 Sept. 2010. IEEE, 2010, pp. 1–7. doi: 10.1109/IPIN.2010.5646207Indoor Localization of Unmanned Aerial Vehicles Based on RSSI / B. R. Stojkoska, J. Palikrushev, K. Trivodaliev, S. Kalajdziski // 17th Intern. Conf. on Smart Technologies. IEEE EUROCON, Ohrid, Macedonia, 06–08 July 2017. IEEE, 2017. P. 120–125. doi: 10.1109/EUROCON.2017.8011089Stojkoska B. R., Palikrushev J., Trivodaliev K., Kalajdziski S. Indoor Localization of Unmanned Aerial Vehicles Based on RSSI. 17th Intern. Conf. on Smart Technologies. IEEE EUROCON, Ohrid, Macedonia, 06–08 July 2017. IEEE, 2017, pp. 120–125. doi: 10.1109/EUROCON.2017.8011089A UAV Patrol System Based on Bluetooth Localization / M. Zhou, J. Lin, S. Liang, W. Du, L. Cheng // 2nd Asia-Pacific Conf. on Intelligent Robot Systems, Wuhan, China, 16–18 June 2017. IEEE, 2017. P. 205–209. doi: 10.1109/ACIRS.2017.7986094Zhou M., Lin J., Liang S., Du W., Cheng L. A UAV Patrol System Based on Bluetooth Localization. 2nd Asia-Pacific Conf. on Intelligent Robot Systems, Wuhan, China, 16–18 June 2017. IEEE, 2017, pp. 205–209. doi: 10.1109/ACIRS.2017.7986094Unmanned Quadcopter Control Using a Motion Capture System / L. L. Gomes, L. Leal, T. R. Oliveira, J. P. V. S. Cunha, T. C. Revoredo // IEEE Latin America Transactions. 2016. Vol. 14, iss. 8. P. 3606–3613. doi: 10.1109/TLA.2016.7786340Gomes L. L., Leal L., Oliveira T. R., Cunha J. P. V. S., Revoredo T. C. Unmanned Quadcopter Control Using a Motion Capture System. IEEE Latin America Transactions. 2016, vol. 14, iss. 8, pp. 3606–3613. doi: 10.1109/TLA.2016.7786340Vision-Controlled Micro Flying Robots / D. Scaramuzza, M. C. Achtelik, L. Doitsidis et al. // IEEE Robotics & Automation Magazine. 2014. Vol. 21, iss. 3. P. 26–40. doi: 10.1109/MRA.2014.2322295Scaramuzza D., Achtelik M. C., Doitsidis L. et al. Vision-Controlled Micro Flying Robots. IEEE Robotics & Automation Magazine. 2014, vol. 21, iss. 3, pp. 26–40. doi: 10.1109/MRA.2014.2322295Ghassemlooy Z., Popoola W., Rajbhandari S. Optical Wireless Communications: System and Channel Modelling with MATLAB®. 2nd Ed. Boca Raton, 2019. 540 p. doi: 10.1201/9781315151724Ghassemlooy Z., Popoola W., Rajbhandari S. Optical Wireless Communications: System and Channel Modelling with MATLAB®. 2nd Ed. Boca Raton, 2019, 540 p. doi: 10.1201/9781315151724LIDAR-inertial integration for UAV localization and mapping in complex environments / R. Opromolla, G. Fasano, G. Rufino, M. Grassi, A. Savvaris // Intern. Conf. on Unmanned Aircraft Systems (ICUAS), Arlington, USA, 07–10 June 2016. IEEE, 2016. P. 649–656. doi: 10.1109/ICUAS.2016.7502580Opromolla R., Fasano G., Rufino G., Grassi M., Savvaris A. LIDAR-Inertial Integration for UAV Localization and Mapping in Complex Environments. Intern. Conf. on Unmanned Aircraft Systems (ICUAS), Arlington, USA, 07–10 June 2016. IEEE, 2016, pp. 649–656. doi: 10.1109/ICUAS.2016.7502580Ole R., Lars G. LiDAR from drones employed for mapping archaeology – potential, benefits and challenges // Archaeological Prospection. 2018. Vol. 25, iss. 4. P. 329–338. doi: 10.1002/arp.1712Ole R., Lars G. LiDAR from Drones Employed for Mapping Archaeology – Potential, Benefits and Challenges. Archaeological Prospection. 2018, vol. 25, iss. 4, pp. 329–338. doi: 10.1002/arp.1712LIDAR/MEMS IMU integrated navigation (SLAM) method for a small UAV in indoor environments / R. Li, J. Liu, L. Zhang, Y. Hang // DGON Inertial Sensors and Systems (ISS), Karlsruhe, Germany, 16–17 Sept. 2014. IEEE, 2014. P. 1–15. doi: 10.1109/InertialSensors.2014.7049479Li R., Liu J., Zhang L., Hang Y. LIDAR/MEMS IMU Integrated Navigation (SLAM) Method for a Small UAV in Indoor Environments. DGON Inertial Sensors and Systems (ISS), Karlsruhe, Germany, 16–17 Sept. 2014. IEEE, 2014, pp. 1–15. doi: 10.1109/InertialSensors.2014.7049479DronOS: Aflexible open-source prototyping framework for interactive drone routines / M. Hoppe, M. Burger, A. Schmidt, T. Kosch // Intern. Conf. on Mobile and Ubiquitous Multimedia (MUM), 26 Nov. 2019. P. 1–7. doi: 10.1145/3365610.3365642Hoppe M., Burger M., Schmidt A., Kosch T. DronOS: Aflexible Open-Source Prototyping Framework for Interactive Drone Routines. Intern. Conf. on Mobile and Ubiquitous Multimedia (MUM), 26 Nov. 2019, pp. 1–7. doi: 10.1145/3365610.3365642Greiff M., Robertsson A., Berntorp K. Performance bounds in positioning with the vive lighthouse system // IEEE Intern. Conf. on Information Fusion (FUSION), Ottawa, Canada, 02–05 July 2019. IEEE, 2019. P. 1–8. doi: 10.23919/FUSION43075.2019.9011242Greiff M., Robertsson A., Berntorp K. Performance Bounds in Positioning with the Vive Lighthouse System. IEEE Intern. Conf. on Information Fusion (FUSION), Ottawa, Canada, 02–05 July 2019. IEEE, 2019, pp. 1–8. doi: 10.23919/FUSION43075.2019.9011242Niehorster D. C., Li L., Lappe M. The accuracy and precision of position and orientation tracking in the HTC vive virtual reality system for scientific research // i-Perception. 2017. Vol. 8, iss. 3. doi: 10.1177/2041669517708205Niehorster D. C., Li L., Lappe M. The Accuracy and Precision of Position and Orientation Tracking in the HTC Vive Virtual Reality System for Scientific Research. I-Perception. 2017, vol. 8, iss. 3. doi: 10.1177/2041669517708205Ikbal M. S., Ramadoss V., Zoppi M. Dynamic pose tracking performance evaluation of HTC vive virtual reality system // IEEE Access. 2021. Vol. 9. P. 3798–3815. doi: 10.1109/ACCESS.2020.3047698Ikbal M. S., Ramadoss V., Zoppi M. Dynamic Pose Tracking Performance Evaluation of HTC Vive Virtual Reality System. IEEE Access. 2021, vol. 9, pp. 3798–3815. doi: 10.1109/ACCESS.2020.3047698Lighthouse Positioning System: Dataset, Accuracy, and Precision for UAV Research / A. Taffanel, B. Rousselot, J. Danielsson, K. McGuire, K. Richardsson, M. Eliasson, T. Antonsson, W. Hönig. URL: https://arxiv.org/pdf/2104.11523 (дата обращения: 12.08.2024)Taffanel A., Rousselot B., Danielsson J., McGuire K., Richardsson K., Eliasson M., Antonsson T., Hönig W. Lighthouse Positioning System: Dataset, Accuracy, and Precision for UAV Research. Available at: https://arxiv.org/pdf/2104.11523 (accessed: 12.08.2024)OptiTrack Motion Capture System. URL: https://www.sdu.dk/en/forskning/sduuascenter/aboutsduuascenter/sduuastestcenter/motioncapturelab (дата обращения: 10.04.2022)OptiTrack Motion Capture System. Available at: https://www.sdu.dk/en/forskning/sduuascenter/aboutsduuascenter/sduuastestcenter/motioncapturelab (accessed: 10.04.2022)OptiTrack for Robotics. URL: https://optitrack.com/applications/robotics/ (дата обращения: 20.01.2024).OptiTrack for Robotics. Available at: https://optitrack.com/applications/robotics/ (accessed: 20.01.2024).

The authors declare that there are no conflicts of interest present.