  This paper describes a system whereby a robot detects and track
human-meaningful navigational cues as it navigates in an indoor environment. It
is intended as the sensor front-end for a mobile robot system that can
communicate its navigational context with human users. From simulated LiDAR
scan data we construct a set of 2D occupancy grid bitmaps, then hand-label
these with human-scale navigational features such as closed doors, open
corridors and intersections. We train a Convolutional Neural Network (CNN) to
recognize these features on input bitmaps. In our demonstration system, these
features are detected at every time step then passed to a tracking module that
does frame-to-frame data association to improve detection accuracy and identify
stable unique features. We evaluate the system in both simulation and the real
world. We compare the performance of using input occupancy grids obtained
directly from LiDAR data, or incrementally constructed with SLAM, and their
combination.
