2024/02/21 by Ho Lyun Jeong, Ziqi Wang, Jeong, Ho Lyun +15 · 1 citation
Social Sciences · #FOS: Computer and information sciences #FOS: Electrical engineering #Geographic Information Systems Studies #Machine Learning (cs.LG) #Robotics (cs.RO) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.14136
openalex publication_date 2024/02/21 · openalex created_date 2024/02/24 · openalex updated_date 2026/07/28
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.