CUHK-AHU Dataset: Promoting Practical Self-Driving Applications in the Complex Airport Logistics, Hill and Urban Environments

摘要

This paper presents a novel dataset targeting three types of challenging environments for autonomous driving, i.e., the industrial logistics environment, the undulating hill environment and the mixed complex urban environment. To the best of the author’s knowledge, similar dataset has not been published in the existing public datasets, especially for the logistics environment collected in the functioning Hong Kong Air Cargo Terminal (HACT). Structural changes always suddenly appeared in the airport logistics environment due to the frequent movement of goods in and out. In the structureless and noisy hill environment, the non-flat plane movement is usual. In the mixed complex urban environment, the highly dynamic residence blocks, sloped roads and highways are included in a single collection. The presented dataset includes LiDAR, image, IMU and GPS data by repeatedly driving along several paths to capture the structural changes, the illumination changes and the different degrees of undulation of the roads. The baseline trajectories are provided which are estimated by Simultaneous Localization and Mapping (SLAM).