GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors
This dataset addresses a data gap for researchers in autonomous building infrastructure, though it is incremental as it provides a new resource rather than a novel method.
The authors tackled the lack of large, time-aligned multimodal datasets for indoor geospatial tracking by introducing GDTM, a nine-hour dataset with distributed sensors and reconfigurable placements, enabling research on multimodal data processing and robustness.
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.