Pole-based Vehicle Localization with Vector Maps: A Camera-LiDAR Comparative Study
Maxime Noizet 
Philippe Xu
Philippe Bonnifait
For autonomous navigation, accurate localization with respect to a map is needed. In urban environments, infrastructure such as buildings or bridges cause major difficulties to Global Navigation Satellite Systems (GNSS) and, despite advances in inertial navigation, it is necessary to support them with other sources of exteroceptive information. In road environments, many common furniture such as traffic signs, traffic lights and street lights take the form of poles. By georeferencing these features in vector maps, they can be used within a localization filter that includes a detection pipeline and a data association method. Poles, having discriminative vertical structures, can be extracted from 3D geometric information using LiDAR sensors. Alternatively, deep neural networks can be employed to detect them from monocular cameras. The lack of depth information induces challenges in associating camera detections with map features. Yet, multi-camera integration provides a cost-efficient solution. This paper quantitatively evaluates the efficacy of these approaches in terms of localization. It introduces a real-time method for camera-based pole detection using a lightweight neural network trained on automatically annotated images. The proposed methods’ efficiency is assessed on a challenging sequence with a vector map. The results highlight the high accuracy of the vision-based approach in open road conditions.
This paper is available on arXiv.
Citation
@inproceedings{noizet2023,
author = {Noizet, Maxime and Xu, Philippe and Bonnifait, Philippe},
title = {Pole-Based {Vehicle} {Localization} with {Vector} {Maps:} {A}
{Camera-LiDAR} {Comparative} {Study}},
booktitle = {2023 IEEE 26th International Conference on Intelligent
Transportation Systems (ITSC)},
pages = {1326 - 1332},
date = {2023-09-08},
url = {https://ieeexplore.ieee.org/document/10422577},
doi = {10.1109/ITSC57777.2023.10422577},
langid = {en},
abstract = {For autonomous navigation, accurate localization with
respect to a map is needed. In urban environments, infrastructure
such as buildings or bridges cause major difficulties to Global
Navigation Satellite Systems (GNSS) and, despite advances in
inertial navigation, it is necessary to support them with other
sources of exteroceptive information. In road environments, many
common furniture such as traffic signs, traffic lights and street
lights take the form of poles. By georeferencing these features in
vector maps, they can be used within a localization filter that
includes a detection pipeline and a data association method. Poles,
having discriminative vertical structures, can be extracted from 3D
geometric information using LiDAR sensors. Alternatively, deep
neural networks can be employed to detect them from monocular
cameras. The lack of depth information induces challenges in
associating camera detections with map features. Yet, multi-camera
integration provides a cost-efficient solution. This paper
quantitatively evaluates the efficacy of these approaches in terms
of localization. It introduces a real-time method for camera-based
pole detection using a lightweight neural network trained on
automatically annotated images. The proposed methods’ efficiency is
assessed on a challenging sequence with a vector map. The results
highlight the high accuracy of the vision-based approach in open
road conditions.}
}