In recent years, floor plan segmentation has gained significant attention due
to its wide range of applications in floor plan reconstruction and robotics. In
this paper, we propose a novel 2D floor plan segmentation technique based on a
down-sampling approach. Our method employs continuous down-sampling on a floor
plan to maintain its structural information while reducing its complexity. We
demonstrate the effectiveness of our approach by presenting results obtained
from both cluttered floor plans generated by a vacuum cleaning robot in unknown
environments and a benchmark of floor plans. Our technique considerably reduces
the computational and implementation complexity of floor plan segmentation,
making it more suitable for real-world applications. Additionally, we discuss
the appropriate metric for evaluating segmentation results. Overall, our
approach yields promising results for 2D floor plan segmentation in cluttered
environments