论文标题
基于相交线的立体声平面猛击
Stereo Plane SLAM Based on Intersecting Lines
论文作者
论文摘要
平面功能是一种稳定的地标，可减少大满贯系统的漂移误差。从密集的点云中提取飞机通常是从RGB-D摄像头或LiDAR获取的，这很容易快速。但是对于立体声摄像机，很难准确有效地计算密集的点云。在本文中，我们提出了一种新的方法，使用从立体图像中提取的相交线来计算平面参数。平面特征通常存在于人造物体和结构的表面上，这些物体和结构具有规则形状和直线线。在3D空间中，两个相交线可以确定这样的平面。因此，我们从立体声左右图像中提取线段。通过立体声匹配，我们计算3D空间中的端点和线方向，然后计算两个相交线的平面。我们在框架跟踪中丢弃了那些不准确的平面特征。在立体声猛击系统中添加此类平面功能可减少漂移误差并完善性能。我们在公共数据集上测试了我们提出的系统，并与最先进的SLAM系统相比，证明了其强大而准确的估计结果。为了使基于飞机的SLAM的研究受益，我们在https://github.com/fishmarch/stereo-plane-slam上发布了代码。
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.
