LiDAR-Based Localization for Formation Control of Multi-Robot Systems

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Recker, T.; Zhou, B.; Stüde, M.; Wielitzka, M.; Ortmaier, T. et al.: LiDAR-Based Localization for Formation Control of Multi-Robot Systems. In: Schüppstuhl, T.; Tracht, K.; Raatz, A. (Eds.): Annals of scientific society for assembly, handling and industrial robotics 2021. Cham : Springer International Publishing, 2022, S. 363-373. DOI: https://doi.org/10.1007/978-3-030-74032-0_30

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/15856

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Sum total of downloads: 43




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Controlling the formation of several mobile robots allows for the connection of these robots to a larger virtual unit. This enables the group of mobile robots to carry out tasks that a single robot could not perform. In order to control all robots like a unit, a formation controller is required, the accuracy of which determines the performance of the group. As shown in various publications and our previous work, the accuracy and control performance of this controller depends heavily on the quality of the localization of the individual robots in the formation, which itself depends on the ability of the robots to locate themselves within a map. Other errors are caused by inaccuracies in the map. To avoid any errors related to the map or external sensors, we plan to calculate the relative positions and velocities directly from the LiDAR data. To do this, we designed an algorithm which uses the LiDAR data to detect the outline of individual robots. Based on this detection, we estimate the robots pose and combine this estimate with the odometry to improve the accuracy. Lastly, we perform a qualitative evaluation of the algorithm using a Faro laser tracker in a realistic indoor environment, showing benefits in localization accuracy for environments with a low density of landmarks.
License of this version: CC BY 4.0 Unported
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
total perc.
1 image of flag of Germany Germany 29 67.44%
2 image of flag of United States United States 9 20.93%
3 image of flag of Russian Federation Russian Federation 3 6.98%
4 image of flag of Israel Israel 1 2.33%
5 image of flag of United Kingdom United Kingdom 1 2.33%

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