ADC: Lidar Localization for Autonomous Driving in Various Weather Conditions

Accurate and robust 6-DoF localization is essential for autonomous vehicles, where LiDAR has emerged as a mainstream alternative to vision-based sensors due to its direct 3D geometric perception. However, existing LiDAR localization methods are primarily designed for clear weather, overlooking severe signal degradation in adverse environments. In rain and snow, precipitation-induced backscattering, laser attenuation, and snow accumulation lead to airborne clutter, point dropouts, and structural corruption in LiDAR observations, severely degrading localization performance. To break this bottleneck, this paper presents ADC, the first map-free localization framework specifically designed for robust LiDAR localization under adverse weather conditions. To combat weather-induced noise and observation inconsistency, we propose a hierarchical sparse encoding architecture coupled with perspective-invariant global aggregation and an adaptive point-wise feature recalibration module to effectively filter out airborne clutter while amplifying stable rigid structures. Furthermore, we introduce a local geometric rigidity constraint that enforces an implicit structural skeleton on degraded point clouds, resolving coordinate drift and preserving topological consistency. Extensive evaluations on the challenging Boreas benchmark and the standard Oxford RobotCar dataset demonstrate that ADC achieves state-of-the-art performance. Specifically, our method reduces mean translation error by 70% on the adverse-weather Boreas dataset and by 18% on the Oxford benchmark relative to the strongest competing map-free baselines, demonstrating robust and weather-resilient localization across diverse driving conditions.