Interior dense 3D reconstruction system with RGB-D camera for complex large scenes

At present, a 3D reconstruction system with simultaneous localization and mapping (SLAM) based on the feature point method presents critical difficulties when the texture is missing. In contrast, with the SLAM based on the direct method, unsatisfactory reconstruction results are achieved when the camera moves at a high speed due to the difficulty in pose estimation. In order to solve such problems, this paper presents a dense 3D scene reconstruction system with a depth camera (RGB-D camera) based on semi-direct SLAM. The system uses the feature point method to estimate the pose of the camera in the rich region of the texture, and then uses an efficient incremental bundle adjustment to optimize the pose of the camera. In areas where the texture is missing, the direct method is used to estimate the pose of the camera. Therefore, the photometric error can be reduced when optimizing the pose of the camera. Then, a 3D map is constructed using the optimized camera pose. The surfel model and the deformation map are used to estimate the pose of the point cloud and the fusion point cloud. The 3D reconstruction system presents the following characteristics: (1) A hand-held camera can be used to scan for 3D reconstruction with any gesture, where the system can reduce the error of reconstruction model caused by human operation; (2) high robustness, stability and strength to deal with the jitter of the missing area and the camera; (3) dense reconstruction of a large scene can be performed, and the reconstruction effect can be well obtained. Multiple experiments show that the proposed system can be applied to 3D reconstruction of various typologies, and can get the optimal 3D reconstruction model. The obtained results prove its applicability in robot navigation, virtual reality shopping malls and other fields. (paper)