构建大规模仿真数据集，提升地面机器人在复杂环境中的感知与导航能力
TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation
- 基于仿真生成910条轨迹，覆盖70种环境，含多模态传感器数据
- 150万样本验证表明现有方法在跨场景泛化上表现不佳
- 适合训练和评估占用预测、SLAM、导航等学习型任务
我们提出TartanGround，一个大规模多模态数据集，用于推动地面机器人在多样化环境中的感知与自主能力。该数据集在多种逼真的仿真环境中采集，包含多个用于360度覆盖的RGB立体相机，以及深度图、光流、立体视差、LiDAR点云、真值位姿、语义分割图像和带语义标签的占据地图。数据通过集成自动化流水线生成，模拟各类地面机器人平台（如轮式与腿式）的运动模式。共收集910条轨迹，覆盖70个环境，总计150万样本。在占据预测和SLAM任务上的评估显示，现有先进方法在跨场景泛化方面表现不足。TartanGround可作为学习型任务（如占据预测、SLAM、神经场景表示、感知导航等）的训练与评估基准，推动机器人感知与自主系统向更鲁棒、更泛化的方向发展。数据集与代码库已公开于https://tartanair.org/tartanground。
原文摘要 · Abstract (English)
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 are available on the webpage: https://tartanair.org/tartanground
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