Cross-Section-Based Method for LiDAR Dataset Generation with Multipath-Resilient Ground Truth

The demand for high-quality LiDAR datasets is increasing as LiDAR technology is being used in various applications, including autonomous vehicles, robotics, and 3D mapping. However, generating accurate ground truth data for LiDAR datasets remains a challenge due to issues like multipath interference (MPI) and other disturbances. The method for generating high-quality ground truth data for LiDAR applications based on cross-section is introduced is this paper. The key concept is based on precisely registering a digital twin, CAD-based which is later converted to a mesh, with LiDAR depth images. By utilizing selective point usage in its cross-sections, the method demonstrates greater robustness to MPI compared to standard approaches. The technique is evaluated using a dataset of LiDAR-acquired point clouds of a living room scene. The results show that the proposed technique achieves significantly better accuracy in affected regions than standard Iterative Closest Point (ICP) based methods. Additionally, the paper proposes a new set of metrics for evaluating the quality of ground truth data, which is more robust to MPI than standard metrics such as RMSE and Chamfer Distance. The proposed technique is a valuable tool for generating large-scale, high-quality datasets for LiDAR applications. Lastly we compared our dataset with latest available datasets.