Abstract
We present TartanGround, a large-scale, multi-modal dataset to advance the perception and autonomy of ground robots operating in diverse environments. This dataset, collected in various photorealistic simulation environments includes multiple RGB stereo cameras for 360-degree coverage, along with depth, optical flow, stereo disparity, LiDAR point clouds, ground truth poses, semantic segmented images, and occupancy maps with semantic labels. Data is collected using an integrated automatic pipeline, which generates trajectories mimicking the motion patterns of various ground robot platforms, including wheeled and legged robots. We collect 910 trajectories across 70 environments, resulting in 1.5 million samples. Evaluations on occupancy prediction and SLAM tasks reveal that state-of-the-art methods trained on existing datasets struggle to generalize across diverse scenes. TartanGround can serve as a testbed for training and evaluation of a broad range of learning-based tasks, including occupancy prediction, SLAM, neural scene representation, perception-based navigation, and more, enabling advancements in robotic perception and autonomy towards achieving robust models generalizable to more diverse scenarios. The dataset and codebase for data collection will be made publicly available upon acceptance. Webpage: this https URL
Abstract (translated)
我们介绍了TartanGround，这是一个大规模、多模态的数据集，旨在推动在各种环境中运行的地面机器人感知和自主性的进步。该数据集是在多个逼真的模拟环境中收集而成，包括了多台RGB立体相机以实现360度覆盖范围，以及深度信息、光流、立体视差、激光雷达点云、真实姿态、语义分割图像和带有语义标签的占用地图等信息。数据采集采用了一条集成自动管道，该管道生成模仿各种地面机器人平台（包括轮式和腿足式机器人）运动模式的轨迹。我们在70个环境中收集了910条轨迹，总计产生了150万个样本。在占据预测和SLAM任务上的评估结果表明，基于现有数据集训练出的方法难以泛化到不同的场景中。TartanGround可以作为广泛学习任务（包括占据预测、SLAM、神经场景表示、感知导航等）的训练与评测平台，有助于推动机器人感知和自主性的发展，使模型能够适用于更加多样的场景。该数据集及其收集代码将在接受后公开发布。官网：[此URL](此URL)
URL
https://arxiv.org/abs/2505.10696
