cs.ROApr 8, 2026

An RTK-SLAM Dataset for Absolute Accuracy Evaluation in GNSS-Degraded Environments

Authors: Wei Zhang, Vincent Ress, David Skuddis, Uwe Soergel, Norbert Haala

Organizations: Institute for Photogrammetry and Geoinformatics, University of Stuttgart, Germany

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Abstract

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/.

Figures & tables

Figure 1: Top: Equipment setup (left) and overview of checkpoints overlaid on the SLAM map of the Stadtgarten scene (right). Orange marked checkpoints are under open sky, while cyan-marked checkpoints are under GNSS obstruction (e.g. buildings, trees, underpass). Bottom: Absolute 3D error per checkpoint for Stadtgarten Seq. 1 using FAST-LIO-SAM method. Standalone RTK errors grow to tens of meters in GNSS-degraded zones, while offline RTK-SLAM remains mostly below 10 cm.
Figure 2: Sensor platform with coordinate frames (red = xx , green = yy , blue = zz ) for LiDAR, IMU, and camera. GNSS antenna phase center (top) references RTK measurements. The base center (bottom) is matched against checkpoints.
Figure 3: Example camera images overlaid with projected LiDAR points, colorized by depth. Top: Stadtgarten scene near building (left) and the GNSS-denied underpass tunnel (right). Bottom: Construction Hall scene entrance area (left) and the indoor construction hall (right).
Figure 4: Trajectory comparisons for all four sequences on satellite imagery. Rows (a)–(b): Stadtgarten Seq. 1–2; rows (c)–(d): Construction Hall Seq. 1–2. GNSS-degraded zones are annotated. In the Construction Hall indoor environment, the GNSS receiver still obtains occasional measurements, but only at single-point positioning accuracy.
Seq.SLAM-only Rel. [cm]RTK-SLAM Rel. [cm]RTK-SLAM Abs. [cm]
Stadtgarten 161.56.56.8
Stadtgarten 226.47.79.9
Construction 131.422.024.8
Construction 212.28.937.3
Table 5: Comparison of SLAM-only relative ATE vs. RTK-SLAM relative and absolute ATE.
Figure 5: 3D absolute positioning error (log scale) as a function of time (left) and distance (right) to the nearest RTK fix, aggregated over the measurements from all sequences. Regression lines indicate the average drift rate.
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