Fiducial markers are commonly used to localize robots. Most existing systems use standard cameras to detect simple patterns on flat tags. Since depth is not directly sensed, the pose must be inferred from the tag geometry. These systems are low-cost and easy to implement on robotic systems, but their performances suffer in low-light conditions and on computationally constrained processors. We propose using 3D light detection and ranging (LiDAR) scanners to mitigate these issues. The reflectivity measurement provided by most LiDAR sensors provides a simple way to discern geometric differences on surfaces. We utilize this fact to create a custom ''beacon'' with reflective fiducials. Next, we design a high-performance segmentation and localization algorithm to find the 2D pose of a mobile robot. Our experiments proved that our system achieves an average euclidean error of less than 0.063 m at ranges of over 10 m while maintaining a runtime of under 3 ms on a basic single board computer. Additionally, our system is highly occlusion resistant. These results are confirmed with multiple field tests of the system.