TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation
TartanGround is a large-scale multi-modal simulation dataset with 1.44 million samples, supporting perception and navigation tasks for ground robots in diverse environments.
Key Findings
Methodology
This paper introduces TartanGround, a comprehensive simulation dataset generated through Unreal Engine 4 and AirSim, employing an automated trajectory sampling pipeline. The process involves environment mesh export, geometric traversability estimation, and path optimization algorithms like TSP to generate diverse, realistic trajectories for wheeled and legged ground robots. Multi-modal sensor data—including 360-degree stereo RGB, depth, LiDAR point clouds, optical flow, semantic segmentation, and occupancy maps—are synchronized and captured in AirSim. Post-processing ensures data quality, including noise modeling and sensor calibration. The dataset supports multiple perception and navigation tasks, enabling robust training and evaluation of models across various challenging scenarios.
Key Results
- Models trained solely on autonomous driving datasets like nuScenes and Waymo show limited generalization, with IoU scores dropping below 13% in natural environments, indicating poor transferability. In contrast, models trained on TartanGround demonstrate improved robustness, with occupancy prediction IoU reaching 18% in forest scenes and SLAM algorithms maintaining trajectory errors below 0.04 meters/frames in complex terrains.
- SLAM algorithms such as ORB-SLAM3, DPVO, and MAC-VO exhibit significant performance degradation in challenging environments like dense forests and indoor-outdoor transition zones, with average relative translation errors around 0.36 meters per frame and rotation errors exceeding 1.8 degrees, highlighting the dataset's difficulty.
- In occupancy prediction, the use of multi-view semantic fusion significantly outperforms monocular depth projection, especially in urban scenes, with IoU improvements of up to 6%. The dataset's environmental diversity exposes models to occlusions, low visibility, and dynamic obstacles, fostering the development of more generalizable perception algorithms.
Significance
TartanGround addresses a critical gap in robotic perception datasets by providing a large-scale, diverse, and realistic simulation resource tailored for ground robots. Its comprehensive multi-modal data supports the development of models capable of operating reliably across unstructured, semi-structured, and urban environments. This has profound implications for advancing autonomous navigation in real-world scenarios such as forests, farms, and construction sites, where existing datasets fall short. Moreover, it facilitates research in multi-task learning, transfer learning, and robustness, accelerating the deployment of autonomous systems in complex settings. The dataset’s ability to simulate challenging conditions like occlusion, variable lighting, and terrain variability makes it a valuable benchmark for future innovations.
Technical Contribution
The paper's key technical contributions include the development of an automated, geometry-based trajectory sampling pipeline that ensures comprehensive environmental coverage and realistic robot motion patterns. It innovatively combines environment mesh export, point cloud sampling, and multi-layered traversability estimation to generate feasible paths for diverse robot types. The integration of multi-modal sensor simulation within Unreal Engine 4 and AirSim enables high-fidelity data collection, including synchronized RGB, depth, LiDAR, and semantic labels. The dataset supports multi-task benchmarking, notably in occupancy prediction and SLAM, with a focus on generalization across environments. These advancements provide a scalable, flexible platform for training robust perception models, pushing the boundaries of simulation-based data generation for robotic perception.
Novelty
This work is the first to systematically generate a large-scale, multi-environment simulation dataset specifically designed for ground robots with realistic motion trajectories. Unlike prior datasets such as TartanAir, which focus on aerial or indoor scenarios, TartanGround emphasizes diverse outdoor environments with complex terrains and occlusions, capturing authentic robot motion patterns. Its automated pipeline for trajectory sampling, environment modeling, and multi-modal sensor simulation is novel, enabling scalable data generation with high environmental fidelity. This comprehensive approach bridges the gap between simulation and real-world robot perception, offering a new benchmark for evaluating generalization and robustness in robotic perception algorithms.
Limitations
- 虽然TartanGround提供了丰富的模拟数据，但其依赖于虚拟环境，可能存在与真实场景的差异，影响模型的实际迁移性能。
- 轨迹采样主要基于静态环境几何信息，未充分模拟动态障碍物和复杂交互场景，未来需引入动态环境模拟以增强模型鲁棒性。
- 传感器模拟参数固定，未充分考虑硬件噪声和误差，可能影响模型在实际硬件上的表现。
Future Work
未来将引入动态障碍物和天气变化模拟，丰富环境多样性，提升模型在复杂真实场景中的适应性。同时，计划结合真实机器人平台，进行跨域迁移学习和模型微调，以实现模拟到现实的无缝迁移。还将扩展多机器人协作轨迹采样，支持多智能体系统的感知与导航研究。进一步优化传感器模型，模拟硬件噪声和误差，增强模型鲁棒性。
AI Executive Summary
在机器人技术快速发展的今天，赋予机器人自主感知和导航能力成为研究的核心目标。现有的感知数据集如KITTI、nuScenes和Waymo，主要面向城市交通环境，推动了自动驾驶技术的进步，但在多样化的非结构化环境中表现不足。森林、农田、施工现场等复杂场景中，遮挡、低能见度和地形变化极大增加了感知难度。传统数据集难以满足这些需求，限制了模型的泛化能力。为此，本文提出了TartanGround，一个基于高仿真模拟的多模态大规模数据集，旨在支持地面机器人在多样环境中的感知与自主导航。
该数据集由70个复杂环境中的878条轨迹组成，涵盖1.44百万样本，提供360度多视角RGB、深度、LiDAR、光流、语义分割和占用地图等多模态信息。利用Unreal Engine 4与AirSim平台，结合自动轨迹采样和路径优化算法（如TSP），模拟多种机器人运动模式（轮式、腿式），确保轨迹空间的充分覆盖。数据采集过程中，模拟传感器的动态特性，保证数据的真实性和多样性。后续处理包括同步校验、噪声模拟和多传感器融合，生成高质量标注。
在占用预测和SLAM两个任务中，实验验证了该数据集在提升模型泛化能力方面的潜力。模型在复杂环境中的表现优于传统训练方法，尤其在遮挡和低能见度场景中表现出更强的鲁棒性。结果显示，环境多样性和真实运动轨迹的引入，有效缓解了模型在未知环境中的性能下降问题。该数据集为机器人感知研究提供了新的平台，推动多任务、多环境、多模态感知算法的发展。未来，将扩展动态环境模拟，增强模型在真实复杂场景中的适应性，并结合实际机器人平台进行跨域迁移，推动模拟到现实的无缝迁移。TartanGround的发布，标志着面向多环境、多任务的地面机器人感知与导航研究迈出了重要一步。
Deep Analysis
Background
机器人感知技术的演变经历了从单一传感器到多模态融合的过程。早期的SLAM算法如ORB-SLAM、LSD-SLAM依赖单目或双目视觉，受限于环境复杂性。随着深度学习的发展，出现了基于卷积神经网络的深度估计和语义理解模型，如Eigen的深度估计网络和SegNet等，为环境理解提供了强大工具。自动驾驶行业的KITTI、nuScenes和Waymo数据集极大推动了城市场景感知的研究，但在非结构化环境中的应用仍受限。模拟数据集如TartanAir引入了多环境、多模态数据，为泛化能力提供了新途径，但多集中于无人机或室内场景，缺乏对地面机器人运动轨迹的模拟。近年来，研究者开始关注多环境、多任务的感知数据集，试图突破模型在复杂环境中的性能瓶颈。TartanGround正是在此背景下，结合先进的模拟技术和自动轨迹采样，提供了多样化、真实感强的地面机器人感知数据。
Core Problem
现有的感知模型在多样化环境中的泛化能力不足，尤其是在森林、农田等非结构化场景中表现差强人意。传统数据集缺乏环境多样性，模型容易过拟合特定场景，导致在新环境中出现遮挡、低能见度等问题时性能骤降。SLAM算法在复杂地形和遮挡条件下的鲁棒性也有限，难以实现可靠的自主导航。此外，模拟数据虽能提供丰富标注，但缺乏真实运动轨迹和多模态传感器的结合，限制了模型的泛化能力。解决这些问题的关键在于构建一个规模大、环境多样、运动轨迹真实、传感器多模态的模拟数据集，以提升模型的适应性和鲁棒性。
Innovation
本研究的主要创新点包括：1）自动轨迹采样流程，结合几何可行性估算和路径优化算法（如TSP），模拟多样化的地面机器人运动轨迹，确保轨迹空间的全面覆盖；2）利用Unreal Engine 4和AirSim平台，模拟高仿真的多模态传感器数据，包括RGB、深度、LiDAR、光流和语义标签，保证数据的真实性和多样性；3）设计多环境、多任务基准，支持占用预测和SLAM等关键任务，验证模型在复杂环境中的泛化能力。这些创新突破了传统依赖真实采集的局限，为机器人自主感知提供了强有力的模拟平台。
Methodology
- �� 环境建模：利用Unreal Engine 4导出环境网格，采样点云，排除非可行区域，确保几何准确性。
- �� 轨迹采样：基于环境点云，采用几何可行性估算，生成多层次的可行路径，结合TSP算法优化路径顺序，确保轨迹空间最大化覆盖。
- �� 轨迹生成：采用多种运动模型（全向、差动驱动、腿式机器人）进行路径插值，生成连续平滑轨迹，模拟真实机器人运动。
- �� 数据采集：在AirSim中同步采集多模态传感器数据，包括RGB、深度、LiDAR、光流、语义标签，确保时间同步和空间一致性。
- �� 后处理：进行同步校验、噪声模拟和传感器参数调节，生成高质量标注数据，包括占用地图和真实位姿。
- �� 质量验证：通过光流一致性检测和碰撞检测，确保数据的准确性和可靠性。
Experiments
实验设计围绕占用预测和SLAM两个核心任务展开。占用预测部分，采用基于多视角融合的深度学习模型（如SurroundOcc），在nuScenes和本数据集环境上训练，评估在城市和自然环境中的IoU指标。SLAM任务中，测试ORB-SLAM3、DPVO和MAC-VO在森林、城市场景中的轨迹追踪误差，分析模型鲁棒性。数据集划分为训练集和测试集，确保模型在多样环境中的泛化能力。通过多模态融合和路径优化，验证模型在遮挡、低能见度等极端条件下的表现。实验结果显示，模型在复杂环境中的性能优于传统方法，验证了数据集的有效性。
Results
在占用预测任务中，SurroundOcc模型在城市环境中的IoU达到19.2%，在森林环境中仅为12.5%，显示出环境多样性对模型性能的影响。SLAM算法方面，ORB-SLAM3在森林场景中频繁失踪，平均平移误差达0.36米/帧，旋转误差1.86°/帧，而DPVO和MAC-VO在复杂环境中表现更优，平均误差分别为0.04米/帧和0.012°/帧。多模态融合技术显著提升了模型在遮挡和低能见度场景中的鲁棒性。整体来看，数据集的多样性和真实运动轨迹极大增强了模型的泛化能力，为未来机器人感知算法提供了坚实基础。
Applications
该数据集适用于自主导航、环境感知、路径规划和避障等多种场景，特别是在森林、农田、施工现场等非结构化环境中。通过多模态数据训练，模型能在复杂环境中实现更准确的感知与定位，提升自主能力。未来，结合实际机器人平台，进行迁移学习和模型微调，将推动机器人在未知环境中的自主适应能力。此外，该数据集还支持多机器人协作、环境理解和智能决策等研究，具有广泛的工业应用潜力。
Limitations & Outlook
尽管TartanGround提供了丰富的模拟数据，但其依赖虚拟环境，可能存在与真实场景的差异，影响模型的实际迁移效果。此外，轨迹采样主要基于静态几何信息，未充分模拟动态障碍物和复杂交互场景，未来需引入动态环境模拟以增强鲁棒性。传感器模拟参数固定，未充分考虑硬件噪声和误差，可能影响模型在实际硬件上的表现。最后，模拟环境的多样性仍有限，未来应扩展更多复杂场景和天气条件，提升模型的泛化能力。
Plain Language Accessible to non-experts
想象你在一个大型的游乐场里玩耍，那里有很多不同的区域，比如过山车区、碰碰车区、迷宫和水上乐园。每个区域都不一样，有的地方很亮，有的地方很暗，有的空间很大，有的很狭窄。你手里拿着一只智能机器人，它就像你的好伙伴，需要知道自己在哪里、周围有什么障碍物，才能安全地玩耍。
以前，科学家们用相机和雷达在游乐场里拍摄，收集很多数据，训练机器人认路、避障。但是这些数据只是在特定的区域拍的，不能让机器人在新环境中表现得很好。现在，研究人员用电脑模拟出一个虚拟的游乐场，把各种不同的区域都建出来，然后让机器人在虚拟游乐场里跑步，采集各种传感器数据，比如彩色相机、深度相机、激光雷达等。这些虚拟数据就像是给机器人准备的“练习场”，让它学会在不同环境中认路、避障。
他们还设计了一套自动让机器人跑轨迹的方法，就像规划一条最短的路线，让机器人在虚拟游乐场里跑得既多又全，确保每个角落都被探索到。这样，机器人就能学会在各种环境中自主行动，不怕遇到新场景。这个虚拟数据集就像是给机器人准备的“训练场”，让它变得更聪明、更可靠。未来，这些技术还能帮机器人在真实世界中工作，比如森林、农田、工地，完成各种复杂任务。
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 are available on the webpage: https://tartanair.org/tartanground
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