A Large-Scale Diverse GNSS/SINS Dataset: Construction, Publication, and Application

Abstract

High-precision continuous position and attitude determination are the critical modules of mobile mapping and autonomous driving (AD). Research in the integration of Global Navigation Satellite System (GNSS) and strapdown inertial navigation system (SINS) has greatly enhanced the accuracy and robustness of position and attitude in different scenes. However, the complexity and variability of the real scenes are still challenging for the existing models, parameters, strategies, and algorithms (MPSA). It is worth noting that high-quality datasets are key to accelerating the research and development of MPSA, which has been proved in the computer vision (CV) fields represented by the ImageNet dataset. Unfortunately, current public datasets either do not provide the raw observations of GNSS and inertial measurement unit (IMUs) or are not collected in abundant scenes and moving platforms. Therefore, a large-scale diverse GNSS/SINS dataset, named SmartPNT-POS, is presented. This dataset covers rich real-world environments, such as open-sky and complex urban, and multiple moving platforms, such as aircraft, land vehicles, and ships. In addition, different types of IMUs, including those manufactured in Hexagon, iMAR Navigation GmbH, and Honeywell, are contained in SmartPNT-POS as well. Moreover, it provides ground truths in each group of data for users to analyze and evaluate their MPSA. Now, the dataset is publicly available through Kaggle, a data science community, and the website to obtain the dataset is provided in the text. There have been 30 sets of data published on the website up to the present, and comprehensive analyses have been made in this contribution for the position and attitude determination results obtained by different processing modes. More data will be collected for different environments and applications and published on the same website in the future.