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.

翻译：本文提出了一种基于体素的方法，用于创建城市环境的数字孪生体，以实现对智慧空间的高效管理。论文阐述了点云数据集的配准与定位流程，该流程使用KISS ICP算法进行扫描点云的拼接，并采用RANSAC方法对拼接后的点云进行初始对齐。以Riegl VMX-250采集的移动测绘点云作为参考地图，并利用Velodyne扫描数据进行定位。随后采用点对面迭代最近点法进一步优化对齐精度。通过计算估计位置与真实位置之间的误差，本文评估了所提方法的有效性。结果表明，基于体素的方法能够精确估计传感器平台的位置，适用于多种应用场景。文中以智慧停车位管理作为具体应用案例进行说明，并展示了初步的可视化结果。