Sensor Calibration and Data Analysis of the MuFoRa Dataset

Valentino Behret, Regina Kushtanova, Islam Fadl, Simon Weber, Thomas Helmer, Frank Palme

2025

Abstract

Autonomous driving sensors face significant challenges under adverse weather conditions such as fog and rain, which can seriously degrade their performance and reliability. Existing datasets often lack the reproducible and measurable data needed to adequately quantify these effects. To address this gap, a new multimodal dataset (MuFoRa) has been collected under controlled adverse weather conditions at the CARISSMA facility, using a stereo camera and two solid-state LiDAR sensors. This dataset is used to quantitatively assess sensor degradation by measuring the entropy for images and the number of inliers for point clouds on a spherical target. These metrics are used to evaluate the impact on performance under varying conditions of fog (5 to 150 m visibility) and rain (20 to 100 mm/h intensity) at different distances (5 to 50 m). Additionally, two calibration target detection approaches extemdash Deep-learning and Hough-based extemdash are evaluated to achieve accurate sensor alignment. The contributions include the introduction of a new dataset focused on fog and rain, the evaluation of sensor degradation, and an improved calibration approach. This dataset is intended to support the development of more robust sensor fusion and object detection algorithms for autonomous driving.

Paper Citation

in Harvard Style

Behret V., Kushtanova R., Fadl I., Weber S., Helmer T. and Palme F. (2025). Sensor Calibration and Data Analysis of the MuFoRa Dataset. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 622-631. DOI: 10.5220/0013310400003912

in Bibtex Style

@conference{visapp25,
author={Valentino Behret and Regina Kushtanova and Islam Fadl and Simon Weber and Thomas Helmer and Frank Palme},
title={Sensor Calibration and Data Analysis of the MuFoRa Dataset},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={622-631},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013310400003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Sensor Calibration and Data Analysis of the MuFoRa Dataset
SN - 978-989-758-728-3
AU - Behret V.
AU - Kushtanova R.
AU - Fadl I.
AU - Weber S.
AU - Helmer T.
AU - Palme F.
PY - 2025
SP - 622
EP - 631
DO - 10.5220/0013310400003912
PB - SciTePress