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.

翻译：目前存在众多用于评估和比较同时定位与地图构建（SLAM）算法的数据集与基准测试。然而，这些数据集的精度必须与近年来SLAM算法的改进速度保持同步。此外，现有数据集在可复现性以及记录轨迹精度或准确度的评估方面，缺乏全面的数据采集协议。基于此目标，我们提出了全站仪机器人地面真值数据集（RTS-GT），以支持基于六自由度（DOF）地面真值轨迹的定位研究。这一新颖数据集包含了利用三台全站仪（RTSs）系统追踪移动机器人平台所生成的六自由度地面真值轨迹。同时，我们还将基于RTS的系统与全球导航卫星系统（GNSS）方案的性能进行了比较。该数据集涵盖17个月内在多种条件下进行的约60次实验，轨迹总长超过49公里，是迄今为止规模最大的基于全站仪测量的数据集。此外，我们提供了每次实验中所有位姿的精度数据——这一特征在现有最先进数据集中尚属空白。实验结果表明，在各种环境条件下，全站仪提供的测量值稳定性是GNSS的22倍，使其成为SLAM基准测试开发的宝贵资源。