  We study algorithms for detecting and including glass objects in an
optimization-based Simultaneous Localization and Mapping (SLAM) algorithm in
this work. When LiDAR data is the primary exteroceptive sensory input, glass
objects are not correctly registered. This occurs as the incident light
primarily passes through the glass objects or reflects away from the source,
resulting in inaccurate range measurements for glass surfaces. Consequently,
the localization and mapping performance is impacted, thereby rendering
navigation in such environments unreliable. Optimization-based SLAM solutions,
which are also referred to as Graph SLAM, are widely regarded as state of the
art. In this paper, we utilize a simple and computationally inexpensive glass
detection scheme for detecting glass objects and present the methodology to
incorporate the identified objects into the occupancy grid maintained by such
an algorithm (Google Cartographer). We develop both local (submap level) and
global algorithms for achieving the objective mentioned above and compare the
maps produced by our method with those produced by an existing algorithm that
utilizes particle filter based SLAM.
