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.

翻译：平面特征是一种稳定的里程碑,可以减少SLAM系统中的漂流错误,从密度点云中提取飞机是容易和快速的,这种云通常从RGB-D摄像机或利达尔获得。但是,对于立体相机来说,很难准确和有效地计算密度点云。在本文中,我们提出了一个新颖的方法,用从立体图像中提取的交叉线计算飞机参数。平面特征通常存在于人造物体和结构表面,这些物体和结构有固定形状和直边线。在3D空间中,两条交叉线可以决定这种飞机。因此,我们从立体立体匹配中提取左方和右方立体图像的线段。我们通过立体匹配,在3D空间中计算终点和线方向,然后从两条交叉线中计算飞机方向。我们在框架跟踪中抛弃这些不准确的飞机特征。在立体SLAM系统中添加这种飞机特征可以减少漂浮误,并改进性能。我们在公共数据集上测试我们提议的系统,并展示其可靠和准确的估算结果,与SLAMMM-SLAS-M-SLAS-SAS-SAS-SAS-SAS-SAS-M-M-M-SLADAR-SAR-SLM系统相比,我们发布了/SL-SAR-SAR-SAR-SD-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-SAR-M-M-M-M-SDIS-M-M-SAR的系统的研究。