High-precision camera pose estimation and optimization in a large-scene 3D reconstruction system

In order to obtain high-precision 3D models for 3D reconstruction of large low-texture scenes, a high-precision camera pose-estimation and optimization method is proposed in this paper. The method mainly uses a grid motion statistics feature-matching algorithm which calculates the number of matching points in the neighborhood to determine whether a match is correct or not. Therefore, this method can ensure that the initial value of the estimated camera pose has high accuracy. In the subsequent camera pose optimization, the image sequence is divided into several sub-sequences and each sub-sequence is independently assigned and optimized, which better solves the problem of camera pose drift verification when the cumulative error is gradually increased. The pose estimation and optimization method can not only obtain the initial value of the camera pose with high precision in the sparse region of the texture, but also solve the problem of reducing the cumulative error with the increase of the scene to obtain the high-precision camera pose when reconstructing the large scene. In our experiments, the size of the selected scene is generally larger than 100 square meters. The proposed methods and current state-of-the-art algorithms were compared quantitatively and qualitatively with published datasets and our own data sets in experiments. In six datasets, the average absolute trajectory error of the method in this paper is 0.014 m, which is smaller than Elasticfusion's result of 0.02 m (Elasticfusion is the method with the smallest pose error in the methods compared in this paper). The proposed scheme can obtain a high-precision camera pose and high-quality 3D reconstruction model, which can be widely applied in robotics, driverless vehicles and virtual reality.