  Numerous datasets and benchmarks exist to assess and compare Simultaneous
Localization and Mapping (SLAM) algorithms. Nevertheless, their precision must
follow the rate at which SLAM algorithms improved in recent years. Moreover,
current datasets fall short of comprehensive data-collection protocol for
reproducibility and the evaluation of the precision or accuracy of the recorded
trajectories. With this objective in mind, we proposed the Robotic Total
Stations Ground Truthing dataset (RTS-GT) dataset to support localization
research with the generation of six-Degrees Of Freedom (DOF) ground truth
trajectories. This novel dataset includes six-DOF ground truth trajectories
generated using a system of three Robotic Total Stations (RTSs) tracking moving
robotic platforms. Furthermore, we compare the performance of the RTS-based
system to a Global Navigation Satellite System (GNSS)-based setup. The dataset
comprises around sixty experiments conducted in various conditions over a
period of 17 months, and encompasses over 49 kilometers of trajectories, making
it the most extensive dataset of RTS-based measurements to date. Additionally,
we provide the precision of all poses for each experiment, a feature not found
in the current state-of-the-art datasets. Our results demonstrate that RTSs
provide measurements that are 22 times more stable than GNSS in various
environmental settings, making them a valuable resource for SLAM benchmark
development.
