FSG: A statistical approach to line detection via fast segments grouping

Iago Suárez, Enrique Muñoz, José M. Buenaposada, Luis Baumela

Abstract:

Line extraction is a preliminary step in various
visual robotic tasks performed in low textured scenes such as
city and indoor settings. Several efficient line segment detection
algorithms such as LSD and EDLines have recently emerged.
However, the state of the art segment grouping methods are
not robust enough or not amenable for detecting lines in real-
time. In this paper we present FSG, a fast and robust line
detection algorithm. It is based on two independent components.
A proposer that greedily cluster segments suggesting plausible
line candidates and a probabilistic model that decides if a group
of segments is an actual line. In the experiments we show that
our procedure is more robust and faster than the best methods
in the literature and achieves state-of-the art performance in
a high level robot localization task such as vanishing points
detection.