  Recent work has shown impressive localization performance using only images
of ground textures taken with a downward facing monocular camera. This provides
a reliable navigation method that is robust to feature sparse environments and
challenging lighting conditions. However, these localization methods require an
existing map for comparison. Our work aims to relax the need for a map by
introducing a full simultaneous localization and mapping (SLAM) system. By not
requiring an existing map, setup times are minimized and the system is more
robust to changing environments. This SLAM system uses a combination of several
techniques to accomplish this. Image keypoints are identified and projected
into the ground plane. These keypoints, visual bags of words, and several
threshold parameters are then used to identify overlapping images and revisited
areas. The system then uses robust M-estimators to estimate the transform
between robot poses with overlapping images and revisited areas. These
optimized estimates make up the map used for navigation. We show, through
experimental data, that this system performs reliably on many ground textures,
but not all.
