  For autonomous navigation, high definition maps are a widely used source of
information. Pole-like features encoded in HD maps such as traffic signs,
traffic lights or street lights can be used as landmarks for localization. For
this purpose, they first need to be detected by the vehicle using its embedded
sensors. While geometric models can be used to process 3D point clouds
retrieved by lidar sensors, modern image-based approaches rely on deep neural
network and therefore heavily depend on annotated training data. In this paper,
a 2D HD map is used to automatically annotate pole-like features in images. In
the absence of height information, the map features are represented as pole
bases at the ground level. We show how an additional lidar sensor can be used
to filter out occluded features and refine the ground projection. We also
demonstrate how an object detector can be trained to detect a pole base. To
evaluate our methodology, it is first validated with data manually annotated
from semantic segmentation and then compared to our own automatically generated
annotated data recorded in the city of Compi{\`e}gne, France. Erratum: In the
original version [1], an error occurred in the accuracy evaluation of the
different models studied and the evaluation method applied on the detection
results was not clearly defined. In this revision, we offer a rectification to
this segment, presenting updated results, especially in terms of Mean Absolute
Errors (MAE).
