Abstract
In this paper, we address some methods for self-positioning. In the work of a large complicated room such as a machine room of a large scale factory and a large ship, it is necessary for a worker to grasp the present position. If a worker who is not necessarily familiar with the internal structure in the room cannot rapidly grasp the self-position, the work efficiency is greatly degraded. Therefore, the self-positioning method of a worker is required. The self-positioning methods such as SLAM (Simultaneously Localization And Mapping) have been investigated so far; however, they are suitable for self-position estimation of a robot, not a human. It is difficult to estimate the position by SLAM only from inaccurate information such as fluctuation and noise. To solve this problem, we design two self-positioning methods. One method is based on similarity between the point cloud of the environmental map and the point cloud taken by a smartphone with a ranging sensor of a worker. The other self-positioning method is based on similarity between the point cloud of environmental map and the point cloud estimated from only RGB image. Furthermore, we evaluate the methods by applying actual environment.
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Kuwamura, H., Ide, T., Miwa, H. (2022). Self-positioning Method Based on Similarity Between Environmental Map and Information of Image and Point Cloud. In: Barolli, L., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2022. Lecture Notes in Networks and Systems, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-031-14627-5_10
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DOI: https://doi.org/10.1007/978-3-031-14627-5_10
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