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：现有多个数据集和基准用于评估与比较同步定位与地图构建（SLAM）算法，但其精度必须跟上近年来SLAM算法提升的速度。此外，当前数据集在可复现性及记录轨迹精度的评估方面缺乏全面的数据采集协议。为此，我们提出机器人全站仪地面真值数据集（RTS-GT），通过生成六自由度（DOF）地面真值轨迹来支持定位研究。该新型数据集包含利用三台机器人全站仪（RTS）系统跟踪移动机器人平台生成的六自由度地面真值轨迹。同时，我们将基于RTS的系统与全球导航卫星系统（GNSS）方案进行了性能比较。本数据集涵盖17个月内在多种条件下进行的约60次实验，轨迹总长度超过49公里，是迄今为止规模最大的RTS测量数据集。此外，我们还提供了每次实验所有位姿的精度指标，这是当前最优数据集所不具备的特点。结果表明，在不同环境条件下，RTS的测量稳定性是GNSS的22倍，使其成为SLAM基准开发的宝贵资源。