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arXiv 2412.08096cs.RO
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THUD++:面向移动机器人的大规模动态室内场景数据集与基准

THUD++: Large-Scale Dynamic Indoor Scene Dataset and Benchmark for Mobile Robots

Shenzhen International Graduate School, Tsinghua University(清华大学深圳国际研究生院);Department of Computer Science and Technology, Tsinghua University(清华大学计算机科学与技术系)

Zeshun Li, Fuhao Li, Wanting Zhang, Zijie Zheng, Xueping Liu, Yongjin Liu, Long Zeng

AI总结:

针对现有移动机器人数据集多为静态、难以评估动态环境性能的问题,本文提出包含真实与合成数据的大规模THUD++动态室内数据集及配套仿真平台,并在多类任务上验证了其对机器人算法研发的推动价值。

摘要:

现有大多数移动机器人数据集主要采集静态场景,限制了其在评估机器人动态环境下性能方面的效用。为解决这一问题,我们提出了一个面向移动机器人的大规模室内数据集,名为THUD++(清华大学动态)机器人数据集,用于动态场景理解。\n目前该数据集包含13个大规模动态场景,结合了分别通过真实机器人平台和物理仿真平台采集的真实数据与合成数据。该RGB-D数据集包含超过9万帧图像、2000万个静态与动态物体的2D/3D边界框、相机位姿以及IMU数据;轨迹数据集覆盖了室内场景中超过6000条行人轨迹。此外,该数据集还配套了一个基于Unity3D的仿真平台,研究人员可借助其创建自定义场景,并在受控环境中测试算法。\n我们在THUD++上针对主流室内场景理解任务(如3D目标检测、语义分割、重定位、行人轨迹预测以及导航)评估了当前最优方法。实验凸显了移动机器人在室内环境中面临的挑战,尤其是在复杂、拥挤且动态的场景中导航时的挑战。通过共享该数据集,我们旨在推动移动机器人算法的开发与测试,为机器人的实际应用提供助力。

英文摘要:

Most existing mobile robotic datasets primarily capture static scenes, limiting their utility for evaluating robotic performance in dynamic environments. To address this, we present a mobile robot oriented large-scale indoor dataset, denoted as THUD++ (TsingHua University Dynamic) robotic dataset, for dynamic scene understanding. Our current dataset includes 13 large-scale dynamic scenarios, combining both real-world and synthetic data collected with a real robot platform and a physical simulation platform, respectively. The RGB-D dataset comprises over 90K image frames, 20M 2D/3D bounding boxes of static and dynamic objects, camera poses, and IMU. The trajectory dataset covers over 6,000 pedestrian trajectories in indoor scenes. Additionally, the dataset is augmented with a Unity3D-based simulation platform, allowing researchers to create custom scenes and test algorithms in a controlled environment. We evaluate state-of-the-art methods on THUD++ across mainstream indoor scene understanding tasks, e.g., 3D object detection, semantic segmentation, relocalization, pedestrian trajectory prediction, and navigation. Our experiments highlight the challenges mobile robots encounter in indoor environments, especially when navigating in complex, crowded, and dynamic scenes. By sharing this dataset, we aim to accelerate the development and testing of mobile robot algorithms, contributing to real-world robotic applications.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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