GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors

arXiv:2402.14136v1 Announce Type: cross
Abstract: 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 …

arxiv cs.lg cs.ro dataset distributed eess.sp geospatial multimodal sensors tracking type