The presence of mobile robots in indoor environments is becoming increasingly significant. While there are algorithms such as SLAM that allow robots to navigate these environments, they do not take into account structural elements of modern buildings such as large glass areas. Due to the use of light-based sensors such as laser scans, these elements are only visible at few incident angles, so they are seen as noise by SLAM algorithms. In this work, reflective surfaces are detected by analyzing laser scan intensity values in order to build a reflection-aware map for safe indoor robot navigation. The main novelty of the proposed method is the usage of a single sensor to gather environment information and a single parameter to determine where reflective zones are. Experiments are carried out in multiple complex real scenarios, quantitatively testing how well the method performs against state of the art works and against factors such as changes in robot orientation, a large number of reflective areas or changes in illumination.