Fast UAV Image Mosaicking by a Triangulated Irregular Network of Bucketed Tiepoints

Abstract

To take full advantage of rapidly deployable unmanned aerial vehicles (UAVs), it is essential to effectively compose many UAV images into one observation image over a region of interest. In this paper, we propose fast image mosaicking using a triangulated irregular network (TIN) constructed from tiepoints. We conduct pairwise tiepoint extraction and rigorous bundle adjustment to generate rigorous tiepoints. We apply a bucketing algorithm to the tiepoints and generate evenly distributed tiepoints. We then construct a TIN from the bucketed tiepoints and extract seamlines for image stitching based on the TIN. Image mosaicking is completed by mapping UAV images along the seamlines onto a reference plane. The experimental results showed that the image mosaicking based on a TIN of bucketed tiepoints could produce image mosaics with stable and fast performance. We expect that our method could be used for rapid image mosaicking.

Seamline Optimization Based on Triangulated Irregular Network of Tiepoints for Fast UAV Image Mosaicking

“…Table 3 shows the results of TIN construction using the initial point clouds. First, the initial dense point cloud was reduced by bucketing for TIN generation with a moderate number of facets [17]. For Dataset 1, 3922 points were sampled from the initial point cloud.…”

“…These results indicate that it is possible to automatically determine the best images for error-prone region improvement based on the unsuitability proposed here. Figures [15][16][17] show the target error-prone regions defined in Section 3.2 and the selection process of the candidate UAV images for updating these regions. In the figures, candidate images are shown by their ranks.…”

“…In our previous work, we developed a fast image stitching technique using triangulated irregular networks (TINs) as a hybrid method of terrain-based and image-based mosaicking methods [17]. In our technique, tiepoints were extracted using the GPU-based speeded up robust features (SURF) algorithm.…”