Map-aided annotation for pole base detection
Maxime Noizet 
Philippe Xu
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 Compiègne, France. .
This paper is available on arXiv.
Citation
@inproceedings{missaoui2023,
author = {Missaoui, Benjamin and Noizet, Maxime and Xu, Philippe},
title = {Map-Aided Annotation for Pole Base Detection},
booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)},
date = {2023-06-04},
url = {https://ieeexplore.ieee.org/document/10186774},
doi = {10.1109/IV55152.2023.10186774},
langid = {en},
abstract = {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
Compiègne, France. .}
}