  Plane feature is a kind of stable landmark to reduce drift error in SLAM
system. It is easy and fast to extract planes from dense point cloud, which is
commonly acquired from RGB-D camera or lidar. But for stereo camera, it is hard
to compute dense point cloud accurately and efficiently. In this paper, we
propose a novel method to compute plane parameters using intersecting lines
which are extracted from the stereo image. The plane features commonly exist on
the surface of man-made objects and structure, which have regular shape and
straight edge lines. In 3D space, two intersecting lines can determine such a
plane. Thus we extract line segments from both stereo left and right image. By
stereo matching, we compute the endpoints and line directions in 3D space, and
then the planes from two intersecting lines. We discard those inaccurate plane
features in the frame tracking. Adding such plane features in stereo SLAM
system reduces the drift error and refines the performance. We test our
proposed system on public datasets and demonstrate its robust and accurate
estimation results, compared with state-of-the-art SLAM systems. To benefit the
research of plane-based SLAM, we release our codes at
https://github.com/fishmarch/Stereo-Plane-SLAM.
