Lidar Pole Detection Training using Vector Maps for Localization

Autonomous navigation requires accurate and reliable localization. In urban environments, infrastructure such as buildings and bridges disrupts Global Navigation Satellite Systems (GNSS), which requires the implementation of robust perception systems combined with inertial navigation. Roadside poles like traffic signs or light poles can serve as stable landmarks for map-based localization. When georeferenced in high-definition vector maps, these features enable reliable localization through detection pipelines and data association methods. While lidar captures their 3D geometry, distinguishing mapped poles in raw point clouds remains challenging. To train pole detectors tailored to the specific map used, we propose an automatic annotation framework that integrates lidar data, a vector map, and offline semantic segmentation to generate precise labeled data. By combining annotated pole clusters from the map with semantic segmentation, annotation errors can be minimized. This enables the training of a map-specific classifier optimized to detect mapped poles while filtering out irrelevant structures. It eliminates the need for manual labeling and ensures adaptability to the map used for online localization. Thanks to real data acquired in real-world urban scenarios, we show that this approach enhances significantly localization accuracy.