RTK-SLAM systems integrate simultaneous localization and mapping (SLAM) with real-time kinematic (RTK) GNSS positioning, promising both relative consistency and globally referenced coordinates for efficient georeferenced surveying. A critical and underappreciated issue is that the standard evaluation metric, Absolute Trajectory Error (ATE), first fits an optimal rigid-body transformation between the estimated trajectory and reference before computing errors. This so-called SE(3) alignment absorbs global drift and systematic errors, making trajectories appear more accurate than they are in practice, and is unsuitable for evaluating the global accuracy of RTK-SLAM. We present a geodetically referenced dataset and evaluation methodology that expose this gap. A key design principle is that the RTK receiver is used solely as a system input, while ground truth is established independently via a geodetic total station. This separation is absent from all existing datasets, where GNSS typically serves as (part of) the ground truth. The dataset is collected with a handheld RTK-SLAM device, comprising two scenes. We evaluate LiDAR-inertial, visual-inertial, and LiDAR-visual-inertial RTK-SLAM systems alongside standalone RTK, reporting direct global accuracy and SE(3)-aligned relative accuracy to make the gap explicit. Results show that SE(3) alignment can underestimate absolute positioning error by up to 76\%. RTK-SLAM achieves centimeter-level absolute accuracy in open-sky conditions and maintains decimeter-level global accuracy indoors, where standalone RTK degrades to tens of meters. The dataset, calibration files, and evaluation scripts are publicly available at https://rtk-slam-dataset.github.io/.

翻译：RTK-SLAM系统将即时定位与地图构建（SLAM）与实时动态（RTK）GNSS定位相结合，兼顾相对一致性与全局参考坐标，适用于高效的地理参考测量。一个关键但未被充分认识的问题是，标准评估指标——绝对轨迹误差（ATE）——在计算误差之前，首先会将估计轨迹与参考轨迹之间拟合最优刚体变换。这种所谓的SE(3)对齐吸收了全局漂移和系统误差，使轨迹看起来比实际更准确，因此不适用于评估RTK-SLAM的全局精度。我们提出一个大地测量参考数据集和评估方法，以揭示这一缺陷。关键设计原则是：RTK接收器仅作为系统输入使用，而真值通过大地全站仪独立建立。这种分离是现有所有数据集所缺失的——在这些数据集中，GNSS通常作为（部分）真值。该数据集采用手持式RTK-SLAM设备采集，包含两个场景。我们评估了激光雷达-惯性、视觉-惯性以及激光雷达-视觉-惯性RTK-SLAM系统，并与独立RTK进行对比，报告了直接全局精度和SE(3)对齐相对精度，以明确展示差异。结果表明，SE(3)对齐可能使绝对定位误差低估高达76%。在开阔天空条件下，RTK-SLAM可达到厘米级绝对精度；在室内，当独立RTK退化为数十米级误差时，它仍能保持分米级全局精度。该数据集、标定文件和评估脚本已公开于https://rtk-slam-dataset.github.io/。