GREAT Dataset: A Multi-Sensor Raw Observation Dataset for High-Precision Urban Navigation
In recent years, multi-sensor fusion technology has emerged as a key approach for high-accuracy localization in intelligent driving. Although numerous multi-sensor datasets have been published, few of them pay enough attention to providing high-precision data, which is essential for assisting integrated methods to achieve the stringent demands of localization. To address this gap, we present the GREAT Dataset, a multi-sensor dataset with raw observations collected on a vehicle-mounted platform in urban environments. The sensor suite comprises a multi-frequency, multi-constellation Global Navigation Satellite System (GNSS) receiver, a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU), a tactical-grade IMU, a stereo camera, and a 16-beam Light Detection and Ranging (LiDAR). All of these sensors are hardware synchronized to the Global Positioning System (GPS) time. The proposed dataset provides both pseudorange and carrier-phase observations of GNSS, along with the tactical-grade IMU measurements. Eight sequences are contained in the GREAT Dataset, captured in urban environments like campuses, urban canyons, and suburban areas. While such diverse sequences pose challenges to different types of sensors, the high-quality data remains potential for algorithms to achieve high-accuracy positioning results, thereby meeting the localization requirements of intelligent driving applications. Besides, we conducted evaluations on a series of representative algorithms to confirm the validity of the dataset. The results also demonstrate that our dataset is suitable for testing multi-sensor integrated approaches. To benefit the research community, we have made the data and relevant scripts publicly available. The download links could be found at https://github.com/GREAT-WHU/GREAT-Dataset Note to Practitioners—This work aims to support multi-sensor fusion algorithms in achieving the strict requirements of localization in autonomous driving. The existing open-sourced multi-sensor datasets often lack sufficient consideration of data precision, which is crucial for accurately evaluating positioning capabilities. To bridge this deficiency, we propose the GREAT dataset, a high-precision multi-sensor dataset with raw observations collected in urban environments. By utilizing this dataset, evaluations can be conducted through comparison of positioning results against our provided high-precision reference solutions. Furthermore, to facilitate researchers’ efforts, we have performed evaluations on a series of representative algorithms. The results demonstrate that the GREAT dataset not only serves as a robust benchmark for assessing localization performance but also presents challenges for achieving high-accuracy positioning, thereby promoting advances in integrated algorithm development.