Optimization algorithm for high precision RGB-D dense point cloud 3D reconstruction in indoor unbounded extension area

Aimed at the problems existing in the present Red Green Blue-Depth (RGB-D) three-dimensional (3D) reconstruction algorithms in the unbounded extension area, such as low accuracy, inaccurate pose estimation, and more restrictions on data set shooting, an optimization algorithm for indoor unbounded RGB-D dense point cloud 3D reconstruction with high accuracy is proposed. The algorithm aims at obtaining better pose estimation during image construction. In the image preprocessing stage, normal direction information is given to each point cloud. In camera pose estimation, since perspective-n-points (PNPs) pose estimation is more accurate and has a smaller cumulative error than the traditional near-point iterative algorithm, this paper improves PNP pose estimation and puts it into the pose estimation algorithm. Direct average distribution of errors to achieve loop closure will affect the accuracy of pose estimation. In this study, Similarity Transformation of 3 Points was used to optimize the solution before global Bundle adjustment, enhancing the closed-loop performance of the algorithm. Experimental verification showed that the error of the proposed algorithm for indoor environment reconstruction was about 2 cm at macro and small scales, and the reconstruction error was less than 2%. It can be widely used for RGB-D 3D reconstruction of large indoor scenes and has high accuracy in pose estimation and mapping.