Conventional mobile robots rely on pre-built point cloud maps for online localization. These map points are generally built using specialized mapping techniques, which involve high labor and computational costs. While in the archi-tectural, engineering and construction (AEC) industry, as-planned building information modelings (BIM) are available for management and operation. In this paper, we consider the use of the digital representations of BIM for robot lo-calization in built environments. First, we convert BIM data into localization-oriented point clouds, which is easy to imple-ment and operate compared to relatively complex SLAM sys-tems. Then, we perform iterative closest point (ICP)-based localization on the metric map using a laser scanner. The experiments are tested using collected laser data and BIM in the real world. The results show that ICP-based localization can track the robot pose with low errors (< [0.20m, 2.50?]), thus demonstrating the feasibility of BIM-based robot local-ization. In addition, we also discuss the reasons for errors, including the deviations between as-planned BIM and as-built status.