基于ORB-SLAM的室内机器人定位和三维稠密地图构建

针对在室内机器人定位和三维稠密地图构建系统中，现有方法无法同时满足高精度定位、大范围和快速性要求的问题，应用具有跟踪、地图构建和重定位三平行线程的ORB-SLAM算法估计机器人三维位姿；然后拼接深度摄像头KINECT获得的三维稠密点云，提出空间域上的关键帧提取方法剔除冗余的视频帧；接着提出子地图法进一步减少地图构建的时间，最终提高算法的整体速度。实验结果表明，所提系统能够在大范围环境中准确定位机器人位置，在运动轨迹为50 m的大范围中，机器人的均方根误差为1.04 m，即误差为2%，同时整体速度为11帧/秒，其中定位速度达到17帧/秒，可以满足室内机器人定位和三维稠密地图构建的精度、大范围和快速性的要求。

Indoor robot localization and 3D dense mapping based on ORB-SLAM

In the indoor robot localization and 3D dense mapping, the existing methods can not satisfy the requirements of high-precision localization, large-scale and rapid mapping. The ORB-SLAM (Oriented FAST and Rotated BRIEF-Simultaneous Localization And Mapping) algorithm, which has three parallel threads including tracking, map building and relocation, was used to estimate the three-dimensional (3D) pose of the robot. And then 3D dense point cloud was obtained by using the depth camera KINECT. The key frame extraction method in spatial domain was introduced to eliminate redundant frames, and the sub-map method was proposed to reduce the cost of mapping, thereby the whole speed of the algorithm was improved. The experiment results show that the proposed method can locate the robot position accurately in a large range. In the range of 50 meters, the root-mean-square error of the robot is 1.04 m, namely the error is 2%, the overall speed is 11 frame/s, and the localization speed is up to 17 frame/s. The proposed method can meet the requirements of indoor robot localization and 3D dense mapping with high precision, large-scale and rapidity.