Multi-robot raster map fusion without initial relative position
Robotic Intelligence and Automation
ISSN: 2754-6969
Article publication date: 21 August 2023
Issue publication date: 13 October 2023
Abstract
Purpose
The purpose of this study is to solve the problem of an unknown initial position in a multi-robot raster map fusion. The method includes two-dimensional (2D) raster maps and three-dimensional (3D) point cloud maps.
Design/methodology/approach
A fusion method using multiple algorithms was proposed. For 2D raster maps, this method uses accelerated robust feature detection to extract feature points of multi-raster maps, and then feature points are matched using a two-step algorithm of minimum Euclidean distance and adjacent feature relation. Finally, the random sample consensus algorithm was used for redundant feature fusion. On the basis of 2D raster map fusion, the method of coordinate alignment is used for 3D point cloud map fusion.
Findings
To verify the effectiveness of the algorithm, the segmentation mapping method (2D raster map) and the actual robot mapping method (2D raster map and 3D point cloud map) were used for experimental verification. The experiments demonstrated the stability and reliability of the proposed algorithm.
Originality/value
This algorithm uses a new visual method with coordinate alignment to process the raster map, which can effectively solve the problem of the demand for the initial relative position of robots in traditional methods and be more adaptable to the fusion of 3D maps. In addition, the original data of the map can come from different types of robots, which greatly improves the universality of the algorithm.
Keywords
Acknowledgements
National Natural Science Foundation of China (62173064), Ministry of education joint fund for pre-equipment research (8091B022119), The Fundamental Research Funds for the Central Universities (DUT22JC13).
Citation
Wang, M., Cong, M., Du, Y., Liu, D. and Tian, X. (2023), "Multi-robot raster map fusion without initial relative position", Robotic Intelligence and Automation, Vol. 43 No. 5, pp. 498-508. https://doi.org/10.1108/RIA-04-2022-0095
Publisher
:Emerald Publishing Limited
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