To cope with variable types and quantities of production in factories and a wide variety of orders in distribution centers, the demand for autonomous mobile robots (AMRs), which are not restricted by guide rails, is increasing in place of automated guided vehicles (AGVs). Accurate localization and orientation estimation are necessary to realize a stable and highly efficient AMR transport system. Even if we assume that the cooperative robots compensate for localization and orientation errors generated by AMRs, the errors generated by current AMRs are larger than the operating range of the cooperative robots, and it is necessary to ensure that the errors are at least within the range. To realize this, it is important to investigate how an AMR changes its localization and orientation accuracy while operating and to correct its position and orientation. In this study, we measured the actual pose of an AMR using a ceiling camera while the AMR was operating and examined the accuracy of the AMR’s localization and orientation sequentially when the course geometry changed. We then used these data to correct the position and orientation. It was found that a linear relationship was observed between the orientation estimation error and angular velocity, and the accuracy could be improved by modifying the orientation estimates using this relationship. The accuracy of localization and orientation estimation could be improved by training neural networks with sine and cosine data instead of angular data to learn errors from the ceiling camera and simultaneous localization and mapping (SLAM) data, and correcting errors.

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