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

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arxiv 2404.13600 v1 pith:3CQN27YI submitted 2024-04-21 cs.RO

classification cs.RO
keywords datasetrobotsplanetaryenvironmentsexplorationgranulartail-plusterrains
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation

    cs.RO 2026-02 conditional novelty 7.0 of 10

    GrandTour releases 49 multi-modal legged-robot missions (>10 km, >5 h) with LiDAR, camera, IMU, depth, proprioception, and mm-level RTK-GNSS/total-station ground truth, plus a 52-method state-estimation benchmark.

  2. DiTer++: Diverse Terrain and Multi-modal Dataset for Multi-Robot SLAM in Multi-session Environments

    cs.RO 2024-12 conditional novelty 6.0 of 10

    DiTer++ offers a legged-robot, multi-robot, day/night, multi-modal SLAM dataset with survey-grade prior maps and benchmark evaluations.

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