This study introduces a robust vehicle localization framework designed for GNSS-limited environments. The proposed approach dynamically integrates georeferenced snow poles—fixed markers used to delineate road boundaries in winter with LiDAR-based odometry to enhance vehicle positioning and navigation. By alternating between GNSS data and LiDAR-based localization depending on GNSS signal availability, the framework addresses the challenges of GNSS-denied environments while leveraging sparse GNSS signals when available. A newly developed dataset of 360-degree snow pole images, captured using an Ouster OS2-128 LiDAR sensor, demonstrates the system’s applicability for autonomous driving. The method achieves a median localization error of 8.39m in GNSS-denied conditions, significantly outperforming techniques like FastReg ( 35.68m ), and progressively improves to sub-meter accuracy as GNSS availability increases. The open-source pipeline, to be made available on https://github.com/bdps1989/Snow-pole-based-vehicle-localization, offers a scalable, reliable, and near real-time solution for autonomous navigation in Nordic winter conditions, advancing research in localization under adverse environments.