GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors
<|reference_start|>GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors: 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.<|reference_end|>
@article{jeong2024gdtm:,
    title={GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal
  Sensors},
    author={Ho Lyun Jeong, Ziqi Wang, Colin Samplawski, Jason Wu, Shiwei Fang,
  Lance M. Kaplan, Deepak Ganesan, Benjamin Marlin, Mani Srivastava},
    journal={arXiv preprint arXiv:2402.14136},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14136},
    primaryClass={cs.RO cs.LG eess.SP}
}