Accurate localization in industrial environments is challenging due to factors such as dust and reflections that degrade perception. To overcome these limitations, we propose an environment-independent localization method that relies solely on ultra-wideband (UWB) positioning. Our system employs LiDAR-SLAM in an offline stage to create a global map frame and calibrate the transformation between this frame and the UWB anchors. During operation, the robot estimates its position using a Kalman filter applied to UWB measurements transformed into the map frame. This paper presents a preliminary feasibility study conducted in an office-like environment to verify the core calibration and localization pipeline. The results show that the proposed method effectively aligns UWB positions with a pre-built SLAM map, achieving a 94% reduction in root-mean-square error (RMSE) compared to raw UWB measurements when validated against LiDAR-SLAM ground truth. This initial verification establishes the technical viability of the framework and lays the groundwork for future validation in harsh, large-scale industrial settings.
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