Are We Ready for Planetary Exploration Robots? The TAIL-Plus Dataset for SLAM in Granular Environments

TL;DR

This paper introduces the TAIL-Plus dataset, a comprehensive collection of sensor data from robots navigating deformable sandy terrains, aimed at advancing SLAM algorithms for planetary exploration robots in complex environments.

Contribution

The paper presents the new TAIL-Plus dataset, extending previous work with more diverse sequences, day-night variations, and multi-robot sensor data for SLAM research in granular terrains.

Findings

Dataset includes diverse sandy terrain sequences.

Multi-sensor data supports advanced SLAM development.

Field experiments demonstrate dataset's applicability.

Abstract

So far, planetary surface exploration depends on various mobile robot platforms. The autonomous navigation and decision-making of these mobile robots in complex terrains largely rely on their terrain-aware perception, localization and mapping capabilities. In this paper we release the TAIL-Plus dataset, a new challenging dataset in deformable granular environments for planetary exploration robots, which is an extension to our previous work, TAIL (Terrain-Aware multI-modaL) dataset. We conducted field experiments on beaches that are considered as planetary surface analog environments for diverse sandy terrains. In TAIL-Plus dataset, we provide more sequences with multiple loops and expand the scene from day to night. Benefit from our sensor suite with modular design, we use both wheeled and quadruped robots for data collection. The sensors include a 3D LiDAR, three downward RGB-D cameras, a pair of global-shutter color cameras that can be used as a forward-looking stereo camera, an RTK-GPS device and an extra IMU. Our datasets are intended to help researchers developing multi-sensor simultaneous localization and mapping (SLAM) algorithms for robots in unstructured, deformable granular terrains. Our datasets and supplementary materials will be available at \url{https://tailrobot.github.io/}.