GDTM：一个基于分布式多模态传感器的室内地理空间跟踪数据集
GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors
AI总结：
本文提出GDTM，一个九小时的室内多模态跟踪数据集，采用分布式传感器与可重构布局，用于研究多模态融合架构优化及模型在不利条件和布局变化下的鲁棒性。
摘要：
持续定位移动物体，即地理空间跟踪，对于自主建筑基础设施至关重要。准确且鲁棒的地理空间跟踪通常利用多模态传感器融合算法，这需要来自各种传感器类型的、时间对齐且同步的数据构成的大型数据集。然而，此类数据集并不容易获得。因此，我们提出了GDTM，一个用于多模态物体跟踪的九小时数据集，该数据集采用分布式多模态传感器和可重构的传感器节点布局。我们的数据集支持探索若干研究问题，例如优化用于处理多模态数据的架构，以及研究模型在不利传感条件和传感器布局变化下的鲁棒性。包含本工作代码、样本数据和检查点的GitHub仓库可在https://github.com/nesl/GDTM获取。
英文摘要：
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.
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。
