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Study of the characteristics and application scenarios of three SLAM algorithms based on comparative methods
- 1 The High School Affiliated to Beijing Normal University, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
This paper compares three Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms are the core technology for autonomous navigation and environmental perception of mobile robots. SLAM algorithms are used by mobile robots to perceive the surrounding environment, build up an environment map and position themselves in real-time in an unknown environment. This article first systematically reviews the basic principles of each algorithm based on experiments and studies that have been completed by previous researchers and illustrates their respective unique mechanisms for processing sensor data, map construction, and localization. Subsequently, this paper analyzes the performance differences and characteristics of the three algorithms in practical applications in terms of robustness in complex environments, consumption of computing resources, and accuracy for generated maps. Finally, based on the advantages and disadvantages of each analyzed algorithm, this article summarizes the most suitable and unsuitable usage scenarios of different algorithms in specific situations. Moreover, this article puts forward specific algorithm selection suggestions for different scenarios to help engineers and researchers make more appropriate decisions in actual projects.
Keywords
Hector SLAM, Gmapping, cartographer, algorithm
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Cite this article
Wang,Q. (2024). Study of the characteristics and application scenarios of three SLAM algorithms based on comparative methods. Applied and Computational Engineering,93,22-28.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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