Abstract
The main focus of this paper is to present a case study of a SLAM solution for Automated Guided Vehicles (AGVs) operating in real-world industrial environments. The studied solution, called Gold-fish SLAM, was implemented to provide localization estimates in dynamic industrial environments, where there are static landmarks that are only rarely perceived by the AGVs. The main idea of Gold-fish SLAM is to consider the goods that enter and leave the environment as temporary landmarks that can be used in combination with the rarely seen static landmarks to compute online estimates of AGV poses. The solution is tested and verified in a factory of paper using an eight ton diesel-truck retrofitted with an AGV control system running at speeds up to 3 m/s. The paper includes also a general discussion on how SLAM can be used in industrial applications with AGVs.
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Acknowledgments
The Authors would like to thank the teams from Kollmorgen, Linde Material Handling, and Stora Enso for their contribution to the MALTA project. The Authors would also like to acknowledge the support of the Swedish KK foundation.
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Andreasson, H., Bouguerra, A., Åstrand, B., Rögnvaldsson, T. (2014). Gold-Fish SLAM: An Application of SLAM to Localize AGVs. In: Yoshida, K., Tadokoro, S. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 92. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40686-7_39
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DOI: https://doi.org/10.1007/978-3-642-40686-7_39
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