Benchmarks stand as vital cornerstones in elevating SLAM algorithms within mobile robotics. Consequently, ensuring accurate and reproducible ground truth generation is vital for fair evaluation. A majority of outdoor ground truths are generated by GNSS, which can lead to discrepancies over time, especially in covered areas. However, research showed that RTS setups are more precise and can alternatively be used to generate these ground truths. In our work, we compare both RTS and GNSS systems' precision and repeatability through a set of experiments conducted weeks and months apart in the same area. We demonstrated that RTS setups give more reproducible results, with disparities having a median value of 8.6 mm compared to a median value of 10.6 cm coming from a GNSS setup. These results highlight that RTS can be considered to benchmark process for SLAM algorithms with higher precision.

翻译：基准测试是提升移动机器人中SLAM算法性能的重要基石。因此，确保准确且可重复的真实轨迹生成对于公正评估至关重要。大多数室外真实轨迹由全球导航卫星系统（GNSS）生成，但随着时间的推移，尤其在覆盖区域中，这可能导致偏差。然而，研究表明，机器人全站仪（RTS）设置更为精确，并可替代用于生成这些真实轨迹。在本研究中，我们通过在同一区域内相隔数周和数月进行的一系列实验，比较了RTS和GNSS系统的精度与可重复性。我们证明，RTS设置能提供更可重复的结果，其差异中位值为8.6毫米，而GNSS设置的中位值为10.6厘米。这些结果凸显了RTS可被视为以更高精度标定SLAM算法基准测试过程的技术。