BROWSE
Detail
Journal Article
Snow-Calib: Deep Learning based Camera-LiDAR Extrinsic Calibration Under Snowy Weather Conditions
- Authors
- Muhammad Fairuz Mummtaz, Jaejun Yoo, Muhammad Rangga Aziz Nasution, Ida Bagus Krishna Yoga Utama, Miftahul Khoir Shilahul Umam, Muhammad Alfi Aldolio, Su Mon Ko, Yeong Min Jang
- Issue Date
- 2025-10
- Citation
- IEEE Access, v.13, pp.182751-182762
- ISSN
- 2169-3536
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- The fusion of camera and LiDAR sensors necessitates precise extrinsic calibration; however, existing deep learning methods are often computationally inefficient and perform poorly in dynamic and adverse environments, such as under snowfall. To address these limitations, we propose a novel framework called Snow-Calib that dynamically integrates multimodal data via an optimized architecture, embedding image-based snow removal via attention mechanisms and multi-scale feature fusion to suppress weather-induced noise while preserving structural details. By jointly leveraging the LiDAR’s geometric robustness and denoised RGB imagery, the framework enhances the accuracy of cross-modal feature matching without requiring separate pre-processing. The overall proposed network consists of 11.9 million parameters, categorized as a lightweight calibration network that can reduce computational overhead. Therefore, the model is resilient to environmental distortions and consumes fewer resources, critical for deployment in autonomous systems operating under variable real-world conditions. Experimental results reveal that Snow-Calib achieves an average calibration error of 0.856 cm for translation and 0.23° for rotation.
- KSP Keywords
- Attention mechanism, Autonomous System, Calibration error, Feature fusion, Image-based, Learning methods, LiDAR sensors, Multi-scale feature, Optimized Architecture, Pre-processing, RGB imagery
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
