RTK-SLAM Dataset for GNSS-Degraded Environment Evaluation

An RTK-SLAM dataset for evaluating absolute global positioning accuracy in GNSS-degraded and GNSS-denied environments. The dataset was created by researchers from the Institute for Photogrammetry and Geoinformatics at the University of Stuttgart, Germany, and was last updated in April 2026.

Use Cases

• Benchmark absolute positioning error by comparing RTK-GNSS ground truth against SLAM-estimated trajectories.
• Analyze the impact of GNSS signal degradation on pose estimation accuracy using synchronized sensor data.
• Train or validate sensor fusion models that combine inertial, visual, or LiDAR data with intermittent RTK corrections.
• Evaluate loop closure and map consistency in long-term SLAM operations where GNSS availability is unreliable.

Strengths

• Dataset is specifically designed for absolute accuracy evaluation, a critical metric for robotic navigation.
• Provides data for both GNSS-degraded and GNSS-denied environmental conditions.

Limitations

• Specific sample size, row count, and data volume are not provided.
• The available data fields, column names, and sensor modalities are not detailed in the provided description.

Provenance

Source

Institute for Photogrammetry and Geoinformatics, University of Stuttgart, Germany.

Collection Method

Collected using an RTK-SLAM system, likely involving mobile platforms with RTK-GNSS receivers and other navigation sensors.

Freshness

Last updated in April 2026, indicating recent maintenance.