Stereo Plane SLAM Based on Intersecting Lines

Plane features can be used to reduce drift errors in SLAM systems, especially in indoor environments. It is easy and efficient to extract planes from a dense point cloud, which is commonly generated from a RGB-D camera or a 3D lidar. But when using a stereo camera, it is hard to compute dense point clouds accurately or efficiently. In this paper, we propose a novel method to compute plane parameters using intersecting lines, which are extracted from stereo images. Plane features are commonly extracted from the surface of man-made objects or structures, which have regular shapes and straight edge lines. In three dimensions, two intersecting lines determine a unique plane. Therefore, we extract line segments from both left and right images of a stereo camera. By stereo matching, we compute lines' endpoints and direction vectors, and then a plane from two intersecting lines is calculated. We discard inaccurate plane features in the frame tracking. Adding such plane features in the stereo SLAM system reduces drift errors and refines the performance. Finally, we build a global map consisting of both points and planes, which can reflect real scene structures. We test our proposed system on public datasets and demonstrate its 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.