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# 2024_05_P_CarloneAntonio

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## Abstract

This seminar paper investigates methodologies for identifying significant edges in poly-
gons, with a particular focus on applications in floor plan analysis. The primary ob-
jective is to develop and evaluate an algorithm that can accurately simplify and refine
polygonal representations of rooms, ensuring that key structural details are preserved.
Several prominent algorithms, including the Douglas-Peucker algorithm, the RANSAC
algorithm, and the Line Segment Detector are analysed for their suitability in pro-
cessing floor plans, considering factors such as precision, computational efficiency, and
robustness to noise. The study introduces a novel refinement algorithm that combines
holistic filtering, regression techniques, and a mechanism for closing polygons. This
algorithm is designed to address the shortcomings of existing methods by providing
a more comprehensive approach to edge detection and polygon simplification. The
algorithm is verified through testing on a dataset of floor plans resulting from real
laser scans, with results demonstrating the effectiveness of the proposed approach in
reducing complexity while maintaining essential geometric features. The findings con-
tribute to the field of automated floor plan generation, offering a scale free solution for
simplifying redundant polygonal data while preserving spatial accuracy.