  Construction spaces are constantly evolving, dynamic environments in need of
continuous surveying, inspection, and assessment. Traditional manual inspection
of such spaces proves to be an arduous and time-consuming activity. Automation
using robotic agents can be an effective solution. Robots, with perception
capabilities can autonomously classify and survey indoor construction spaces.
In this paper, we present a novel identification-on-the-fly approach for coarse
classification of indoor spaces using the unique signature of clutter. Using
the context granted by clutter, we recognize common indoor spaces such as
corridors, staircases, shared spaces, and restrooms. The proposed clutter
slices pipeline achieves a maximum accuracy of 93.6% on the presented clutter
slices dataset. This sensor independent approach can be generalized to various
domains to equip intelligent autonomous agents in better perceiving their
environment.
