Robust Path Planning in the Wild for Automatic Look-Ahead Camera Control

Sander Klomp, Sander Klomp, Peter H. N. de With

2023

Abstract

Finding potential driving paths on unstructured roads is a challenging problem for autonomous driving and robotics applications. Although the rise of autonomous driving has resulted in massive public datasets, most of these datasets focus on urban environments and feature almost exclusively paved roads. To circumvent the problem of limited public datasets of unpaved roads, we combine seven public vehicle-mounted-camera datasets with a very small private dataset and train a neural network to achieve accurate road segmentation on almost any type of road. This trained network vastly outperforms networks trained on individual datasets when validated on our unpaved road datasets, with only a minor performance reduction on the highly challenging public WildDash dataset, which is mostly urban. Finally, we develop an algorithm to robustly transform these road segmentations to road centerlines, used to automatically control a vehicle-mounted PTZ camera.

Paper Citation

in Harvard Style

Klomp S. and H. N. de With P. (2023). Robust Path Planning in the Wild for Automatic Look-Ahead Camera Control. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 553-561. DOI: 10.5220/0011614200003417

in Bibtex Style

@conference{visapp23,
author={Sander Klomp and Peter H. N. de With},
title={Robust Path Planning in the Wild for Automatic Look-Ahead Camera Control},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={553-561},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011614200003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Robust Path Planning in the Wild for Automatic Look-Ahead Camera Control
SN - 978-989-758-634-7
AU - Klomp S.
AU - H. N. de With P.
PY - 2023
SP - 553
EP - 561
DO - 10.5220/0011614200003417
PB - SciTePress