Uncertainty-Aware DNN for Multi-Modal Camera Localization

M. Vaghi, A. Ballardini, S. Fontana, D. Sorrenti

2024

Abstract

Camera localization, i.e., camera pose regression, represents an important task in computer vision with many practical applications such as in the context of intelligent vehicles and their localization. Having reliable estimates of the regression uncertainties is also important, as it would allow us to catch dangerous localization failures. In the literature, uncertainty estimation in Deep Neural Networks (DNNs) is often performed through sampling methods, such as Monte Carlo Dropout (MCD) and Deep Ensemble (DE), at the expense of undesirable execution time or an increase in hardware resources. In this work, we considered an uncertainty estimation approach named Deep Evidential Regression (DER) that avoids any sampling technique, providing direct uncertainty estimates. Our goal is to provide a systematic approach to intercept localization failures of camera localization systems based on DNNs architectures, by analyzing the generated uncertainties. We propose to exploit CMRNet, a DNN approach for multi-modal image to LiDAR map registration, by modifying its internal configuration to allow for extensive experimental activity on two different datasets. The experimental section highlights CMRNet’s major flaws and proves that our proposal does not compromise the original localization performances, but also provides the necessary introspection measures that would allow end-users to act accordingly.

Paper Citation

in Harvard Style

Vaghi M., Ballardini A., Fontana S. and Sorrenti D. (2024). Uncertainty-Aware DNN for Multi-Modal Camera Localization. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 80-90. DOI: 10.5220/0013064600003822

in Bibtex Style

@conference{icinco24,
author={M. Vaghi and A. Ballardini and S. Fontana and D. Sorrenti},
title={Uncertainty-Aware DNN for Multi-Modal Camera Localization},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2024},
pages={80-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013064600003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Uncertainty-Aware DNN for Multi-Modal Camera Localization
SN - 978-989-758-717-7
AU - Vaghi M.
AU - Ballardini A.
AU - Fontana S.
AU - Sorrenti D.
PY - 2024
SP - 80
EP - 90
DO - 10.5220/0013064600003822
PB - SciTePress