Photogrammetric mapping missions using robots or unmanned aerial vehicles (UAVs) often encounter environments where sparse strong-texture structures are found alongside extensive weak-texture surfaces, such as indoor corridors with frames and aerial views of shorelines. Establishing accurate correspondences under such circumstances remains a major technical challenge. Traditional methods can only match a few or no correspondences. Deep learning algorithms can yield stable matches in weak-texture regions. However, they rely on large-scale annotated data and computational resources, limiting their real-time application and generalization capability. This article proposes an anchor point–assisted image-matching method, using Euclidean distances and angular relationships between corner points in the weak-texture area and anchor points in the strong-texture area to establish structured features, enhancing the distinctiveness of the corner points in the weak-texture area. Meanwhile, epipolar geometry constraints are applied to restrict the candidate corner points search to a one-dimensional range, thereby improving the accuracy, efficiency, and reliability of the corner point matching. Comparative experiments on indoor, outdoor, and UAV data sets demonstrate that the proposed method achieves a greater number of correct matches than scale-invariant feature transform (SIFT), oriented FAST and rotated BRIEF (ORB), KAZE, AKAZE, grid-based motion statistics (GMS), SuperGlue, and LightGlue with a conventional CPU configuration. The presented method, SuperGlue, and LightGlue achieve a success rate (SR) of 100% on three data sets and secure sufficient correspondences in weak-texture regions. In contrast, the SR obtained by traditional methods ranges from 70% to 80%. In addition, our method runs in under 3 seconds, whereas the deep learning methods still require over 10 seconds even on a lightweight CPU. The effects of anchor point numbers, dual threshold settings, and epipolar search range on matching performance are analyzed to identify the optimal parameter configuration. The proposed method provides potential for robot and UAV photogrammetric mapping applications in sparsely textured environments based on resource-constrained platforms.
Chen et al. (Thu,) studied this question.