Mid Sweden University

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. 

The development of machine learning has resulted in data gaining a pivotal role in the technological advancement, especially data where the ground truth of targeted parameters can be efficiently captured. This requires the development of methods that facilitate accurate data collection with ground truth. Under this perspective, Time of Flight sensors pose a high complexity due to the multifaceted nature of noise in the captured data. To enable the use of such sensors in a wide range of applications including Artificial Intelligence, we need to provide also accurate ground truth data. In this article, we present a method for automated data capturing from a LiDAR sensor together with ground truth data generation. This method will facilitate generating big datasets from LiDAR sensors with high accuracy ground truth data. In addition, we provide a dataset that aside from depth sensor data contains also RGB, confidence and infrared data captured from the LiDAR sensor. As a result, the proposed method not only facilitates data capturing but it enables to generate accurate ground truth data, with RMSE of only 0.04 m at 1.3 m distance. 

Eliminating Multi-Path Interference (MPI) stands as a significant unresolved challenge in the domain of depth estimation using Time-of-Flight (ToF) cameras. ToF data is typically influenced by significant noise and artifacts stemming from MPI. Although a variety of conventional methods have been suggested to enhance ToF data quality, the application of machine learning techniques has been infrequent, primarily due to the scarcity of authentic training data with accurate depth information. This paper introduces an approach that eliminates the dependency on labeled real-world data within the learning framework. We employ a U-Net trained on the data with ground truth in a supervised manner, enabling it to leverage multi-frequency ToF data for MPI correction. Concurrently, we compare three channels as input with one channel and two channels. Our experimental results convincingly showcase the effectiveness of this approach in reducing noise in real-world data.