Constantly locating moving objects, i.e., geospatial tracking, is essential for autonomous building infrastructure. Accurate and robust geospatial tracking often leverages multimodal sensor fusion algorithms, which require large datasets with time-aligned, synchronized data from various sensor types. However, such datasets are not readily available. Hence, we propose GDTM, a nine-hour dataset for multimodal object tracking with distributed multimodal sensors and reconfigurable sensor node placements. Our dataset enables the exploration of several research problems, such as optimizing architectures for processing multimodal data, and investigating models' robustness to adverse sensing conditions and sensor placement variances. A GitHub repository containing the code, sample data, and checkpoints of this work is available at https://github.com/nesl/GDTM.

翻译：持续定位移动物体（即地理空间追踪）是自主建筑基础设施的关键。准确且鲁棒的地理空间追踪通常依赖于多模态传感器融合算法，这类算法需要包含多种传感器类型的时间同步校准数据的大规模数据集。然而，此类数据集目前尚不成熟。为此，我们提出GDTM——一个基于分布式多模态传感器且支持可重构传感器节点部署的九小时多模态物体追踪数据集。该数据集可支持多个研究问题的探索，例如多模态数据处理架构优化，以及模型在恶劣传感条件与传感器布局差异下的鲁棒性研究。本研究的代码、示例数据和模型检查点已开源至GitHub仓库：https://github.com/nesl/GDTM。