GREAT DATASET

Introduction

Abstract

GREAT Dataset is a new multi‑sensor raw observation dataset collected from a vehicle in complex urban environments, featuring high‑precision multi‑frequency GNSS receivers, tactical‑grade IMU, MEMS IMU, two CMOS cameras, and LiDAR. All sensors are hardware‑synchronized and well‑calibrated. The dataset includes eight sequences covering the Wuhan University campus and nearby urban/suburban areas, supporting evaluation of various multi‑sensor fusion navigation algorithms.

Main Contributions

• Provides raw GNSS observations, IMU measurements, camera images, and LiDAR scans with timestamps unified to GPS time via hardware sync.
• Captures diverse environments (campus, urban canyon, suburb) to thoroughly assess SLAM robustness and accuracy.
• Supplies high‑precision raw GNSS and inertial navigation data for robotics, SLAM, and satellite navigation research.

License

The dataset is released under the MIT License for academic use only. Commercial use or collaborations require contacting xingkonggreat@163.com.

Sensor Setup

Platform

Details and diagrams are provided in the original documentation.

Sensors

Comparison with Other Datasets

Environment

Eight sequences are provided, including four day‑time campus runs, one night‑time campus run, two urban canyon runs, and two suburban runs. Sample images illustrate each environment.

Data Sequences

Download

Reference Solutions

Reference trajectories for each sequence have been plotted in Google Earth. Figures 7‑14 in the original document illustrate these trajectories.

Directory Structure

Raw GNSS observations and ephemerides are stored in the GNSS_RAW_DATA folder. Each sequence folder contains IMU data, stereo images, LiDAR scans, and reference solutions.

Development Toolkit

A script is provided to convert raw visual‑LiDAR observations to ROS bag files. The tool requires a ROS environment and necessary dependencies.