Accurate localization and mapping are essential for mobile robots. Using laser rangefinders (LRFs), current state-of-the-art indoor Simultaneous Localization and Mapping (SLAM) can provide accurate real-time localization and mapping in most environments. An exemption are those where glass is predominant, as LRFs can not properly detect glass due to glass' transparency and reflectiveness. With such buildings becoming more common, this has become an important issue to address. Failure to detect glass causes two problems for SLAM: incorrectly mapping glass as open space; and, lower localization accuracy due to mismatches between measured and expected range data. In this paper, we propose a glass confidence map that correctly maps glass as occupied, as well as the probability of an object to be glass/non-glass. Our approach consists of four steps: (i) map all objects, even potential dynamic obstacles, as occupied, (ii) compute the probability of scanned objects to be glass/non-glass using a neural network, (iii) online map updates by matching scanned objects to probability map, and (iv) filter dynamic obstacles and noise. We validated our approach in an office with large glass areas, achieving more than 95% of glass areas correctly mapped as occupied with less than 5% glass/non-glass classification error.