Uncertainty Hypervolume in Point Feature-Based Visual Odometry

InJun Mun, Sukhan Lee

2024

Abstract

Visual odometry based on point feature matching has been well-established. Notably, methods based on essential and fundamental as well as homography matrices have been widely used. It is known that the accuracy of visual odometry is affected by the choice of matched feature point pairs. However, no mathematically rigorous formula relating the choice of feature point pairs to the uncertainty involved in visual odometry is available. Instead, point selection heuristics based on feature point distribution combined with RANSAC-based refinement are mostly adopted to ensure accuracy. In this paper, we present “Uncertainty Hypervolume” as a rigorous mathematical formula that relates the selected feature point pairs to the uncertainty of visual odometry. The uncertainty hypervolume associated with selected feature point pairs provides a precise metric for evaluating the selected feature point pairs and the resulting visual odometry. This metric is useful in practice not only for selecting the best feature point pairs but also for selecting poor feature point pairs available for visual odometry. Furthermore, it accurately identifies the uncertainty in visual odometry, which helps better manage the performance of visual odometry applications.

Paper Citation

in Harvard Style

Mun I. and Lee S. (2024). Uncertainty Hypervolume in Point Feature-Based Visual Odometry. 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 290-299. DOI: 10.5220/0013019300003822

in Bibtex Style

@conference{icinco24,
author={InJun Mun and Sukhan Lee},
title={Uncertainty Hypervolume in Point Feature-Based Visual Odometry},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2024},
pages={290-299},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013019300003822},
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 Hypervolume in Point Feature-Based Visual Odometry
SN - 978-989-758-717-7
AU - Mun I.
AU - Lee S.
PY - 2024
SP - 290
EP - 299
DO - 10.5220/0013019300003822
PB - SciTePress