GRACO: A Multimodal Dataset for Ground and Aerial Cooperative Localization and Mapping

Abstract

Compared with using only a single type of robot, the use of drones and ground vehicles to jointly explore unknown areas can bring efficiency improvements. However, due to the difficulty of ground-aerial loop detection and especially the lack of ground-air datasets in large outdoor scenes, there is no good solution for ground-aerial cooperative localization technology right now. Therefore, this letter introduces a dataset to facilitate the development of multimodal Simultaneous Localization And Mapping (SLAM) as well as ground-air heterogeneous Cooperative SLAM (C-SLAM) algorithms. This dataset was collected by a group of ground and aerial robots equipped with Light Detection and Ranging (LiDAR), cameras, and Global Navigation Satellite/Inertial Navigation Systems (GNSS/INS). All sensors were well-calibrated and triggered by a self-made synchronization module, and the synchronization accuracy can reach millisecond level. The high-precision ground truth is obtained by post-processing the navigation information collected by the GNSS/INS system. There are considerable overlaps in the acquisition routes on the ground and in the air, which facilitates C-SLAM algorithms and loop detection. Also, the special installation angles in the air, and scenes such as channel and roof bring new challenges to the SLAM algorithm. We also tested several well-known algorithms on our dataset.