A ground robot dataset for multi-sensor navigation in diverse environments

Abstract

Reliable absolute positioning remains a challenge in autonomous ground robotics, particularly in complex and dynamic real-world environments. The fusion of drift-free global positioning and precise local positioning is essential to ensure continuous and accurate localization in mobile ground robots. However, a benchmark dataset encompassing challenging scenarios for both absolute and relative positioning is still lacking, which limits further research and comprehensive evaluation of fusion-based Simultaneous Localization and Mapping (SLAM) methods for autonomous ground robots. To fill this gap, we introduce a ground robot dataset for multi-sensor navigation in diverse environments. All sensors are well calibrated, and Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), camera, and Light Detection and Ranging (LiDAR) measurements are hardware-synchronized. Furthermore, several auxiliary sensors are also included in our system, which are often overlooked in existing datasets but may be vital in certain applications. We perform data acquisition in a variety of challenging environments, both outdoors and indoors. In outdoor scenarios, ground truth is provided by a high-level integrated navigation system, while in indoor environments, it is obtained using a motion capture system. We evaluate the positioning performance of several baseline algorithms on our dataset, and the results show that current methods need further improvement in specific challenging scenarios. To advance relevant research, we make the dataset and associated tools publicly available. The project can be accessed at https://github.com/lizhipro/MSN-DE.git.