GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal
  Sensors
Ho Lyun Jeong Ziqi Wang Colin Samplawski Jason Wu Shiwei Fang Lance M. Kaplan Deepak Ganesan Benjamin Marlin Mani Srivastava
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.