  This paper presents a semantic planar SLAM system that improves pose
estimation and mapping using cues from an instance planar segmentation network.
While the mainstream approaches are using RGB-D sensors, employing a monocular
camera with such a system still faces challenges such as robust data
association and precise geometric model fitting. In the majority of existing
work, geometric model estimation problems such as homography estimation and
piece-wise planar reconstruction (PPR) are usually solved by standard (greedy)
RANSAC separately and sequentially. However, setting the inlier-outlier
threshold is difficult in absence of information about the scene (i.e. the
scale). In this work, we revisit these problems and argue that two mentioned
geometric models (homographies/3D planes) can be solved by minimizing an energy
function that exploits the spatial coherence, i.e. with graph-cut optimization,
which also tackles the practical issue when the output of a trained CNN is
inaccurate. Moreover, we propose an adaptive parameter setting strategy based
on our experiments, and report a comprehensive evaluation on various
open-source datasets.
