Dense 3D reconstruction is required for robots to safely navigate or perform advanced tasks. The accurate depth information of the image and its pose are the basis of 3D reconstruction. The resolution of depth maps obtained by LIDAR and RGB-D cameras is limited, and traditional pose calculation methods are not accurate enough. In addition, if each image is used for dense 3D reconstruction, the dense point clouds will increase the amount of calculation. To address these issues, we propose a 3D reconstruction system. Specifically, we propose a depth network of contour and gradient attention, which is used to complete and correct depth maps to obtain high-resolution and high-quality depth maps. Then, we propose a method of fusion of traditional algorithms and deep learning for pose estimation to obtain accurate localization results. Finally, we adopt the method of autonomous selection of keyframes to reduce the number of keyframes, the surfel-based geometric reconstruction is performed to reconstruct the dense 3D environment. On the TUM RGB-D, ICL-NIUM, and KITTI datasets, our method significantly improves the quality of the depth maps, the localization results, and the effect of 3D reconstruction. At the same time, we have also accelerated the speed of 3D reconstruction.

机器人安全导航或执行高级任务需要密集的 3D 重建。图像的准确深度信息及其姿态是3D重建的基础。LIDAR和RGB-D相机获得的深度图分辨率有限，传统的位姿计算方法不够准确。另外，如果每张图像都用于密集的3D重建，密集的点云会增加计算量。为了解决这些问题，我们提出了一个 3D 重建系统。具体来说，我们提出了一个轮廓和梯度注意力的深度网络，用于完成和校正深度图以获得高分辨率和高质量的深度图。然后，我们提出了一种融合传统算法和深度学习的姿态估计方法，以获得准确的定位结果。最后，我们采用自主选择关键帧的方法来减少关键帧的数量，进行基于面元的几何重建来重建密集的3D环境。在 TUM RGB-D、ICL-NIUM 和 KITTI 数据集上，我们的方法显着提高了深度图的质量、定位结果和 3D 重建的效果。同时，我们也加快了3D重建的速度。