  Determining the drivable area, or free space segmentation, is critical for
mobile robots to navigate indoor environments safely. However, the lack of
coherent markings and structures (e.g., lanes, curbs, etc.) in indoor spaces
places the burden of traversability estimation heavily on the mobile robot.
This paper explores the use of a self-supervised one-shot texture segmentation
framework and an RGB-D camera to achieve robust drivable area segmentation.
With a fast inference speed and compact size, the developed model, MOSTS is
ideal for real-time robot navigation and various embedded applications. A
benchmark study was conducted to compare MOSTS's performance with existing
one-shot texture segmentation models to evaluate its performance. Additionally,
a validation dataset was built to assess MOSTS's ability to perform texture
segmentation in the wild, where it effectively identified small low-lying
objects that were previously undetectable by depth measurements. Further, the
study also compared MOSTS's performance with two State-Of-The-Art (SOTA) indoor
semantic segmentation models, both quantitatively and qualitatively. The
results showed that MOSTS offers comparable accuracy with up to eight times
faster inference speed in indoor drivable area segmentation.
