We present a new large dataset of indoor human and robot navigation and interaction, called TH\"OR-MAGNI, that is designed to facilitate research on social navigation: e.g., modelling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. TH\"OR-MAGNI was created to fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, TH\"OR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human-human and human-robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. TH\"OR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human-robot interactions.

翻译：我们提出一个名为THÖR-MAGNI的新型大规模室内人机导航与交互数据集，旨在促进社交导航研究：例如，建模与预测人体运动、分析人与机器人之间的目标导向交互、以及探究社交交互情境下的视觉注意力。THÖR-MAGNI旨在填补现有用于人体运动分析与HRI的数据集的空白。这一空白表现为缺乏对外生因素及核心目标主体线索的全面包含，这阻碍了能够捕捉不同场景中情境线索与人类行为之间关系的鲁棒模型的发展。与现有数据集不同，THÖR-MAGNI包含更广泛的情境特征，并提供多种场景变体以促进因素分离。该数据集涵盖众多社交性人-人与-机器人交互场景、丰富的上下文注释，以及多模态数据（如步行轨迹、眼动追踪数据、以及移动机器人记录的激光雷达和摄像头数据流）。我们还提供一套用于可视化和处理记录数据的工具。据我们所知，THÖR-MAGNI在情境化且社交动态环境中采集的传感器数据量及多样性方面具有独特性，能够捕捉自然的人机交互。