WHU-Helmet: A Helmet-Based Multisensor SLAM Dataset for the Evaluation of Real-Time 3-D Mapping in Large-Scale GNSS-Denied Environments

Real-time 3-D mapping of large-scale global navigation satellite system (GNSS)-denied environments plays an important role in forest inventory management, disaster emergency response, and underground facility maintenance. Compact helmet laser scanning (HLS) systems keep the same direction as the user’s line of sight and have the advantage of “what you see is what you get,” providing a promising and efficient solution for 3-D geospatial information acquisition. However, the violent motion of the helmet, the limited field of view (FoV) of the laser scanner, and the repeated symmetrical geometric structures in the GNSS-denied environments pose enormous challenges for the existing simultaneous localization and mapping (SLAM) algorithms. To promote the development of HLS and explore its application in large-scale GNSS-denied environments, the first large-scale HLS dataset covering multiple difficult GNSS-denied areas (e.g., forests, mountains, underground spaces) was built in this study. Besides using an additional very high accuracy fiber-optic inertial measurement unit (IMU), a novel postprocessing multisource fusion method—progressive trajectory correction (PTC)—is proposed to generate a reliable ground-truth trajectory for the benchmark, which overcomes the problems of scan matching degradation and nonrigid distortion. The accuracies of the ground truth are controlled and checked by manually surveyed feature points along the trajectory. Finally, the existing state-of-the-art SLAM methods were evaluated on the WHU-Helmet dataset, summarizing the future HLS SLAM research trends. The full dataset is available for download at:
https://github.com/kafeiyin00/WHU-HelmetDataset
.