Towards Robust Methodology in Large-Scale Urban Reconstruction

Abstract

The digitization of geographic environments, such as cities and archaeological sites, is of priority interest to the scientific community due to its potential applications. But there are still several issues to address. A comprehensive methodology is presented to reconstruct urban environments using a mobile land platform. All the implemented stages are described, the process to merge several point clouds to build a large-scale map is described, as well as the texture extraction and generation of surfaces, being able to render large urban areas using a low density of points but without losing the details of the structures within the urban scenes. The proposal is evaluated using several metrics, for example, coverage and root-mean-square-error (RMSE). The results are compared against three methodologies reported in the literature, obtaining better results in the 2D/3D data fusion process and the generation of surfaces. Presented method has a low RMSE (0.79) compared to the other methods and a runtime of approximately 40 s to process each data set (point cloud, panoramic image, and inertial data). In general, the proposed methodology shows a more homogeneous density distribution without losing the details, that is, it conserves the spatial distribution of the points, but with fewer data.