基于单目相机的机器人改进SLAM系统

Improved slam system for robot based on monocular camera

针对机器人导航ORB-SLAM2系统存在着点云地图过于稀疏和相机同步跟踪轨迹不够准确的问题,提出了一种基于单目摄像机的半稠密SLAM系统。使用高斯三角测量确定图像点的深度,并且结合极线搜索和块匹配技术得到一组呈概率分布的深度值,最后利用深度滤波器让深度估计收敛到一个稳定的值。经过实验验证,提出的基于单目摄像机的半稠密SLAM系统能够绘制出更为稠密的点云,并且得益于半稠密化的点云,该系统的同步跟踪与定位的精准度比ORB-SLAM2系统提高了9.13%。

Due to the problem that the point cloud map is too sparse and the tracking track of camera is not accurate enough in ORB-SLAM2 system, a semi-dense SLAM system based on a monocular camera is proposed.When a semi-dense map is built with a monocular camera, it is impossible that every pixel can be considered as a feature point to calculate their descriptors.In this paper, Gauss triangulation is used to determine the depth of image points, and a set of depth values with probability distribution is obtained by combining polar line search and block matching techniques.Finally, a depth filter is used to make the depth estimation converge to a stable value.Experimental results show that the proposed semi-dense SLAM system based on monocular camera can map a more dense point cloud, and the synchronization tracking and positioning accuracy of the system is 9.13% higher than that of ORB-SLAM2 system due to the semi-dense point cloud map.