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 — a separation absent from all existing benchmarks. The dataset covers two outdoor-to-indoor scenes with synchronized LiDAR, camera, IMU, and RTK inputs. 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 videos below show recorded sensor data replayed at 4× speed with online FAST-LIO-SAM processing. Top row: 3D local map view with accumulated point cloud and trajectory (left); IMU readings (right). Bottom row: Global map view (left) — ★ stars: surveyed checkpoints, ● red points: GNSS measurements, ● blue trajectory: RTK-SLAM online estimate; GNSS status with RTK fix quality indicators (center); camera image (right).
The handheld RTK-SLAM device integrates:
The LiDAR and its built-in IMU are hardware-synchronized to GNSS time via a 1 PPS signal. All extrinsics are carefully calibrated; the GNSS antenna phase center and device base center are referenced via CAD model offsets, enabling direct comparison of estimated positions against surveyed control points.
Public park in Stuttgart. Two sequences covering 1.04 km and 0.46 km. Three distinct GNSS zones: open sky, partial obstruction (buildings/trees), and a fully GNSS-denied 30 m underpass tunnel. 55 control points total.
OutdoorGNSS-degradedGNSS-denied
IntCDC construction site, University of Stuttgart. Two sequences (clockwise & counter-clockwise) covering 0.48 km and 0.39 km. Each sequence begins and ends outdoors with RTK fix and traverses the interior where GNSS signals are severely degraded (>400 s, ~150 m). 32 control points total.
Outdoor-to-indoorGNSS-severely degraded
Ground truth is established via a two-stage procedure entirely independent of the RTK receiver used as system input. First, open-sky anchor points are surveyed by static GNSS observations (<5 mm std). A Leica TS16 total station is oriented to these anchors and measures all remaining control points, including those under GNSS obstruction and inside GNSS-denied areas, propagating the global reference frame via a traverse. The final ground truth accuracy is better than 1 cm for all control points.
Critical design principle: the RTK receiver is exclusively a system input. Ground truth is established by the total station independently. This separation — absent from all existing benchmarks — is what enables meaningful evaluation of absolute global accuracy.
The standard SE(3)-aligned ATE is unsuitable for evaluating RTK-SLAM. A trajectory that is meters away from its true global position can still yield a near-zero SE(3)-aligned ATE if its internal geometry is consistent. Our evaluation protocol directly compares estimated positions against geodetically surveyed control points — without any alignment — exposing global drift that standard benchmarks would hide. The alignment gap can reach 76%, meaning standard metrics can underestimate the true absolute error by up to a factor of 4.
We benchmark three RTK-SLAM configurations:
Absolute ATE (no alignment) vs. SE(3)-aligned ATE. The Gap column shows how much the alignment hides. A gap of 76% means the standard metric underestimates the true absolute error by a factor of more than 4.
| Scene | Seq. | FAST-LIO-SAM | OKVIS2-X(vig) | OKVIS2-X(lvig) | RTK [m] |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Online [m] | Offline [m] | SE3 [m] | Gap [%] | Online [m] | Offline [m] | SE3 [m] | Gap [%] | Online [m] | Offline [m] | SE3 [m] | Gap [%] | |||
| Stadtgarten | Seq. 1 | 0.162 | 0.068 | 0.065 | 4 | 3.276 | 0.189 | 0.185 | 2 | 4.103 | 0.068 | 0.060 | 12 | 13.98 |
| Stadtgarten | Seq. 2 | 0.150 | 0.099 | 0.077 | 22 | 2.695 | 0.907 | 0.831 | 8 | 3.180 | 0.092 | 0.080 | 13 | 11.99 |
| Constr. Hall | Seq. 1 | 0.256 | 0.248 | 0.220 | 11 | 1.437 | 0.788 | 0.579 | 27 | 0.761 | 0.321 | 0.227 | 29 | 12.01 |
| Constr. Hall | Seq. 2 | 0.439 | 0.373 | 0.089 | 76 | 3.715 | 0.700 | 0.511 | 27 | 0.825 | 0.170 | 0.081 | 52 | 14.84 |
Trajectories for all four sequences overlaid on satellite imagery, with GNSS-denied and GNSS-degraded zones annotated.
Positioning error (log scale) as a function of time elapsed and distance traveled to the nearest RTK fix, aggregated over all checkpoint measurements. Because offline optimization propagates corrections both forward and backward, the x-axis is distance to the nearest RTK fix rather than only since the last fix. LiDAR-aided methods show low drift rates: 9.2 cm/min (0.25% of path) for FAST-LIO-SAM and 8.0 cm/min (0.22%) for OKVIS2-X(lvig). Standalone RTK degrades rapidly once signal quality deteriorates.
The dataset, calibration files, and evaluation scripts are publicly available. Each sequence contains synchronized LiDAR, camera, IMU, and RTK data. All sequences are available in three formats: ROS1 (.bag), ROS2 (.db3), and EuRoC (extended format compatible with OKVIS2-X).
Note on camera timestamps: The camera has a hardware trigger delay of −20.6 ms relative to the IMU clock (estimated by Kalibr). This offset is already compensated in all released formats and no additional time shift is needed.
@article{zhang2026rtkslam,
title={An RTK-SLAM Dataset for Absolute Accuracy Evaluation in GNSS-Degraded Environments},
author={Zhang, Wei and Ress, Vincent and Skuddis, David and Soergel, Uwe and Haala, Norbert},
journal={The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
year={2026}
}