  Monocular simultaneous localization and mapping (SLAM) is emerging in
advanced driver assistance systems and autonomous driving, because a single
camera is cheap and easy to install. Conventional monocular SLAM has two major
challenges leading inaccurate localization and mapping. First, it is
challenging to estimate scales in localization and mapping. Second,
conventional monocular SLAM uses inappropriate mapping factors such as dynamic
objects and low-parallax areas in mapping. This paper proposes an improved
real-time monocular SLAM that resolves the aforementioned challenges by
efficiently using deep learning-based semantic segmentation. To achieve the
real-time execution of the proposed method, we apply semantic segmentation only
to downsampled keyframes in parallel with mapping processes. In addition, the
proposed method corrects scales of camera poses and three-dimensional (3D)
points, using estimated ground plane from road-labeled 3D points and the real
camera height. The proposed method also removes inappropriate corner features
labeled as moving objects and low parallax areas. Experiments with eight video
sequences demonstrate that the proposed monocular SLAM system achieves
significantly improved and comparable trajectory tracking accuracy, compared to
existing state-of-the-art monocular and stereo SLAM systems, respectively. The
proposed system can achieve real-time tracking on a standard CPU potentially
with a standard GPU support, whereas existing segmentation-aided monocular SLAM
does not.
