SLAM in Navigation Systems of Autonomous Mobile Robots

Hanenko, Liudmyla та Storchak, Kamila та Shlianchak, Svitlana та Vorokhob, Maksym та Pitaichuk, Mylana (2025) SLAM in Navigation Systems of Autonomous Mobile Robots Cybersecurity Providing in Information and Telecommunication Systems 2025 (3991). с. 173-182. ISSN 1613-0073

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Анотація

SLAM (Simultaneous Localisation and Mapping) is a fundamental technology in robotics that allows autonomous systems to simultaneously create a map of an unknown environment and determine its location in it. This paper provides a detailed analysis of SLAM algorithms: EKF SLAM (Extended Kalman Filter), FastSLAM, Graph SLAM, SEIF SLAM (Sparse Extended Information Filter), LIDAR SLAM, VSLAM (Visual SLAM) and IMU SLAM. The advantages and disadvantages of these algorithms are considered. The EKF SLAM, FastSLAM, Graph SLAM, and SEIF SLAM algorithms are evaluated by key metrics such as mapping accuracy, localization accuracy, computational complexity, scalability, and convergence speed. Based on the evaluation of SLAM algorithms by these metrics, we compare their performance in different environments and conditions. EKF SLAM, which uses an extended Kalman filter, provides high accuracy but suffers from high computational complexity and sensitivity to linearisation errors. FastSLAM solves some of these problems by using a particle filter to estimate the robot’s trajectory, which reduces the computational load while maintaining high accuracy. Graph SLAM formulates the SLAM problem as a graph optimization problem, which allows for more efficient data association and loop closure handling, although it increases memory usage. SEIF SLAM, using sparse information matrices, balances accuracy and computational efficiency, making it suitable for large environments. LIDAR SLAM provides very high accuracy and robustness in mapping, but its reliance on expensive sensors is a significant drawback. VSLAM uses cameras to collect data, making it less dependent on sophisticated sensors, but vulnerable to changes in lighting and environmental textures. IMU SLAM integrates data from inertial measurement devices, which increases robustness to fast movements but can accumulate errors over time. Based on a comparison of key metrics, the optimal use of each algorithm is suggested depending on the specific conditions.

Тип елементу :	Стаття
Ключові слова:	mobile robots; path planning; SLAM; algorithm; sensors; navigation
Типологія:	Статті у базах даних > Scopus (без квартилю)
Підрозділи:	Факультет інформаційних технологій та математики > Кафедра інформаційної та кібернетичної безпеки ім. професора Володимира Бурячка
Користувач, що депонує:	Павло Миколайович Складанний
Дата внесення:	22 Лип 2025 08:32
Останні зміни:	22 Лип 2025 08:32
URI:	https://elibrary.kubg.edu.ua/id/eprint/52533

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