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

2026-04-08

AI Summary

This study addresses the limitation of existing SE(3) alignment–based evaluation methods, which obscure the absolute positioning errors of RTK-SLAM systems in GNSS-degraded environments. To this end, we present the first GNSS-independent geodetic ground-truth dataset, leveraging a total station to provide centimeter-level reference measurements across diverse indoor and outdoor scenarios. Notably, RTK is incorporated solely as a system input rather than a source of ground truth—a first in the field. The dataset supports benchmarking of lidar-inertial, visual-inertial, and multimodal SLAM systems, and is accompanied by publicly released calibration parameters and evaluation scripts. Experimental results demonstrate that SE(3) alignment can underestimate absolute trajectory error by up to 76%. Furthermore, RTK-SLAM achieves centimeter-level accuracy outdoors and maintains decimeter-level precision indoors, substantially outperforming standalone RTK, which exhibits errors of tens of meters in indoor settings.

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/.

Problem

• RTK-SLAM
• Absolute Trajectory Error
• SE(3) alignment
• global accuracy
• GNSS-degraded environments

Innovation

• RTK-SLAM
• absolute accuracy
• SE(3) alignment bias
• geodetic ground truth
• GNSS-degraded environments