  This paper proposes a voxel-based approach for creating a digital twin of an
urban environment that is capable of efficiently managing smart spaces. The
paper explains the registration and localization procedure of the point cloud
dataset, which uses the KISS ICP for scan point cloud combination and the
RANSAC method for the initial alignment of the combined point cloud. The mobile
mapping point cloud using Riegl VMX-250 serves as the reference map, and
Velodyne scans are used for localization purposes. The point-to-plane iterative
closest-point method is then employed to refine the alignment. The paper
evaluates the efficacy of the proposed method by calculating the errors between
the estimated and ground truth positions. The results indicate that the
voxel-based approach is capable of accurately estimating the position of the
sensor platform, which are applicable for various use cases. A specific use
case in the context is smart parking space management, which is described and
initial visualization results are shown.
