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This paper presents a characterization of sensing failures of light detection and ranging (LIDAR) given the presence of a mirror, which are quite common in our daily lives. Although LIDARs play an important role in the field of robotics, previous research has addressed little regarding the challenges in optical sensing such as mirror reflections. As light can be reflected off a mirror and penetrate a window, mobile robots equipped with LIDARs only may not be capable of dealing with real environments. It is straightforward to deal with mirrors and windows by fusing sensors of heterogeneous characteristics. However, indistinguishability between mirror images and true objects makes the map inconsistent with the true environment, even for a robot with heterogeneous sensors. We propose a Bayesian framework to detect and track mirrors using only LIDAR information. Mirrors are detected by utilizing the property of mirror symmetry. Spatiotemporal information is integrated using a Bayesian filter. The proposed approach can be seamlessly integrated into the occupancy grid map representation and the mobile robot localization framework, and has been demonstrated using real data from a LIDAR. Mirrors, as potential obstacles, are successfully detected and tracked.