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.

中文翻译：

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大场景3D重建系统中高精度相机位姿估计与优化

为了获得高精度的3D模型用于大型低纹理场景的3D重建，本文提出了一种高精度相机位姿估计和优化方法。该方法主要采用网格运动统计特征匹配算法，通过计算邻域内匹配点的个数来判断匹配是否正确。因此，该方法可以保证估计出的相机位姿的初始值具有较高的精度。在后续的相机位姿优化中，将图像序列分成若干个子序列，每个子序列独立分配优化，更好地解决了累积误差逐渐增大时相机位姿漂移验证的问题。位姿估计优化方法不仅可以在纹理的稀疏区域高精度地获得相机位姿的初始值，而且可以解决随着场景的增加而减少累积误差的问题，以获得高精度重建大场景时的相机姿势。在我们的实验中，所选场景的大小一般大于 100 平方米。在实验中将所提出的方法和当前最先进的算法与已发布的数据集和我们自己的数据集进行了定量和定性比较。在六个数据集中，本文方法的平均绝对轨迹误差为0.014 m，小于Elasticfusion的结果0.02 m（Elasticfusion是本文比较方法中位姿误差最小的方法）。