Using SLAM systems in unmanned aircraft complexes to assess situation in emergencies
DOI:
https://doi.org/10.33408/2519-237X.2026.10-2.215Keywords:
artificial intelligence, SLAM method, unmanned aerial vehicles, emergency response, innovative technologiesAbstract
Purpose. To conceptually substantiate and analyze the application of SLAM systems in unmanned aerial vehicles for precise 3D and 4D mapping of objects and terrain in emergency situations to provide timely and accurate information to emergency response managers (ERM).
Methods. A review of existing SLAM architectures, analysis of multi-sensor data fusion methods, comparison of algorithms (LOAM, LeGO-LOAM, LIO-SAM, etc.) based on literature sources and a conceptual evaluation of the results using metrics of mapping accuracy, model resolution, and sustainability to interference.
Findings. Multisensor SLAM systems demonstrating the potential for creating highly accurate 3D models of objects and terrain in extreme conditions were analyzed. It has been established that the use of SLAM for operational mapping in emergency situations allows for increasing the accuracy of information for ERM, reducing the time for making management decisions and minimizing risks during emergency response.
Application field of research. The conceptual conclusions obtained can be used to develop strategies for implementing SLAM systems in unmanned aerial vehicles for monitoring and modeling emergency zones, emergency response radar information systems, and for solving other life safety problems.
References
Shirokov G.S., Satsuk I.V. Primeneniye foto i video s"yemki s ispol'zovaniyem BAS v tselyakh resheniya zadach MCHS Rossii [Application of photo and video shooting using UAS to solve the problems of the EMERCOM of Russia]. Proc. of VI All-Russian scientific-practical conf. «Molodye uchenye v reshenii aktual'nykh problem bezopasnosti», Zheleznogorsk, May 24, 2024. Zheleznogorsk: Siberian Fire and Rescue Academy of EMERCOM of Russia, 2024. Pp. 293–297. (rus). EDN: https://elibrary.ru/LLSMVR.
Xu X., Zhang L., Yang J., Cao C., Wang W., Ran Y., Tan Z., Luo M. A review of multi-sensor fusion slam systems based on 3D LIDAR. Remote Sensing, 2022. Vol. 14, No. 12. Article 2835. DOI: https://doi.org/10.3390/rs14122835.
Kravchenko S.A. Pozhar kak naibolee veroyatnaya chrezvychaynaya situatsiya v gorodskoy srede [Fire as the most likely emergency situation in urban environment]. Proc. of Interuniversity student scientific conf. «Bezopasnost' gorodskoy sredy», Omsk, November 29, 2013. Omsk State Institute of Service. Omsk, 2013. Pp. 17–19. EDN: https://elibrary.ru/TJRYCN.
Yarovoy V.Yu., Shamsudinov G.Yu., Morozov V.V. Ob osobennostyakh programmnogo obespecheniya dlya sredstv vosstanovleniya vidimosti zvena gazodymozashchitnoy sluzhby v zadymlennoy srede [On the peculiarities of software for means of restoring the visibility of the gas smoke protection service link in a smoke-filled environment]. Engineering Journal of Don, 2025. No. 8 (128). Pp. 848–858. EDN: https://elibrary.ru/SCSEPM.
Zhang J., Singh S. LOAM: Lidar odometry and mapping in real-time. Proc. of X Intern. scientific conf. «Robotics: Science and systems», Berkeley, USA, July 12–16, 2014. USA, Berkeley, University of California, 2014. 9 p. DOI: https://doi.org/10.15607/RSS.2014.X.007.
Xue G., Li R., Liu Sh., Wei J. Research on underground coal mine map construction method based on LeGO-LOAM improved algorithm. Energies, 2022. Vol. 15, No. 17. Article 6256, 17 p. DOI: https://doi.org/10.3390/en15176256. EDN: https://elibrary.ru/PXAKZC.
Meng X., Chen X., Chen Sh., Fang Y., Fan H., Luo J., Wu Yu., Sun B. An improved LIO-SAM algorithm by integrating image information for dynamic and unstructured environments. Measurement Science and Technology, 2024. Vol. 35, No. 9. Article 096313. DOI: https://doi.org/10.1088/1361-6501/ad56b1. EDN: https://elibrary.ru/QEEEYD.
Qin X., Zhang X., Yasin M.Q., Wang Sh.,·Feng Zh.,·Xiao G. SUMA: A partial materialization-based scalable query answering in OWL 2 DL. Data Science and Engineering, 2021. Vol. 6, No. 2. Pp. 229–245. DOI: https://doi.org/10.1007/s41019-020-00150-0. EDN: https://elibrary.ru/HERMAO.
van Manen B.R., Sluiter V., Mersha A.Y. FirebotSLAM: Thermal SLAM to increase situational awareness in smoke-filled environments. Sensors, 2023. Vol. 23, No. 17. Article 7611, 25 p. DOI: https://doi.org/10.3390/s23177611.
Zhang J., Xiao R., Li H., Liu Y., Suo X., Hong C., Lin Z., Wang D. 4DRT-SLAM: Robust SLAM in smoke environments using 4D radar and thermal camera based on dense deep learnt features. Proc. of «2023 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)», Penang, Malaysia, June 9–12, 2023. IEEE, 2023. Pp. 19–24. DOI: https://doi.org/10.1109/CIS-RAM55796.2023.10370026.
Dai Y., Zhao M. Grey Wolf resampling-based Rao-Blackwellized particle filter for mobile robot simultaneous localiza-tion and mapping. Journal of Robotics, 2021. Vol. 2021. Article 4978984, 9 p. DOI: https://doi.org/10.1155/2021/4978984. EDN: https://elibrary.ru/EMSUBA.
Khan A., Mineo C., Dobie G., Macleod Ch., Pierce G. Vision guided robotic inspection for parts in manufacturing and remanufacturing industry. Journal of Remanufacturing, 2021. Vol. 11, No. 1. P. 49–70. DOI: https://doi.org/10.1007/s13243-020-00091-x. EDN: https://elibrary.ru/VLCMYB.
Cai X., Xu J., Deng K., Lan H., Wu Yu., ZhuGe X., Yang Zh. TrinitySLAM: On-board real-time event-image fusion SLAM system for drones. ACM Transactions on Sensor Networks, 2024. Vol. 20, No. 6. Article 121, 22 p. DOI: https://doi.org/10.1145/3696420. EDN: https://elibrary.ru/TWVDCE.
Published
How to Cite
License
Copyright (c) 2026 Shamsudinov G.Yu., Yarovoy V.Yu., Mikhaylova A.K.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
