Kavli Affiliate: Zheng Zhu
| First 5 Authors: Zirui Wang, Chen Yao, Yangtao Ge, Guowei Shi, Ningbo Yang
| Summary:
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/}.
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