Efficient SfM for Large-Scale UAV Images Based on Graph-Indexed BoW and Parallel-Constructed BA Optimization

Abstract

Structure from Motion (SfM) for large-scale UAV (Unmanned Aerial Vehicle) images has been widely used in the fields of photogrammetry and computer vision. Its efficiency, however, decreases dramatically as well as with the memory occupation rising steeply due to the explosion of data volume and the iterative BA (bundle adjustment) optimization. In this paper, an efficient SfM solution is proposed to solve the low-efficiency and high memory consumption problems. First, an algorithm is designed to find UAV image match pairs based on a graph-indexed bag-of-words (BoW) model (GIBoW), which treats visual words as vertices and link relations as edges to build a small-world graph structure. The small-world graph structure can be used to search the nearest-neighbor visual word for query features with extremely high efficiency. Reliable UAV image match pairs can effectively improve feature matching efficiency. Second, a central bundle adjustment with object point-wise parallel construction of the Schur complement (PSCBA) is proposed, which is designed as the combination of the LM (Levenberg–Marquardt) algorithm with the preconditioned conjugate gradients (PCG). The PSCBA method can dramatically reduce the memory consumption in both error and normal equations, as well as improve efficiency. Finally, by using four UAV datasets, the effectiveness of the proposed SfM solution is verified through comprehensive analysis and comparison. The experimental results show that compared with Colmap-Bow and Dbow2, the proposed graph index BoW retrieval algorithm improves the efficiency of image match pair selection with an acceleration ratio ranging from 3 to 7. Meanwhile, the parallel-constructed BA optimization algorithm can achieve accurate bundle adjustment results with an acceleration ratio by 2 to 7 times and reduce memory occupation by 2 to 3 times compared with the BA optimization using Ceres solver. For large-scale UAV images, the proposed method is an effective and reliable solution.