基于改进ORB-SLAM2算法的RGB-D稠密地图构建

RGB-D Dense Map Construction Based on Improved ORB-SLAM2 Algorithm

针对传统ORB-SLAM2系统无法稠密建图以及缺少可应用于移动机器人在特定场景下导航与路径规划的八叉树地图等问题,提出了一种改进RGB-D ORB-SLAM2算法.该算法利用深度信息,通过针孔成像模型计算出关键帧点云的三维空间位置,采用离群点滤波去除多余杂点,体素滤波保留具有特征信息的点云进行拼接,降低地图冗余度,并经过稠密回环处理进一步优化更新关键帧的点云位姿,构建出精确的稠密点云地图并转换为八叉树地图.试验数据表明,相比于RGB-D SLAMV2系统,RGB-D ORB-SLAM2系统的全局轨迹误差和相对位姿误差提升有50%以上,均方根误差为0.89%,均值误差为0.76%;在建图性能方面,相比于同类型算法,点云数量平均降低约30%.此外,八叉树地图相比于点云地图,仅占其内存的0.6%,更加满足了高精度、快速性的导航需求.

An improved RGB-D ORB-SLAM2 algorithm is proposed to address the problems that the traditional ORB-SLAM2 system cannot build a dense map and lack an octree map that can be applied to the navigation and path planning of mobile robots in specific scenarios. The algorithm uses depth information to calculate the 3D spatial position of the point clouds in key frames through the pinhole imaging model and uses outlier filtering to remove redundant clutter and voxel filtering to retain point clouds with feature information for stitching and reduce map redundancy. And through dense loopback processing, the point cloud poses are further optimized and updated in keyframes, and an accurate dense point cloud map is constructed and converted into an octree map. The experimental data shows that compared with the RGB-D SLAMV2 system, the global trajectory error and relative positional error of the RGB-D ORB-SLAM2 system are improved by more than 50%, the root mean square error is 0.89%, and the mean error is 0.76%;in terms of map-building performance, the number of point clouds is reduced by about 30%on average when compared with the same type of algorithms. In addition, the octree map occupies only 0.6% of its memory when compared with the point cloud map, which better meets the high precision and fast navigation demands.