Robust Underwater Visual Graph SLAM using a Simanese Neural Network and Robust Image Matching

Antoni Burguera, Antoni Burguera

2022

Abstract

This paper proposes a fast method to robustly perform Visual Graph SLAM in underwater environments. Since Graph SLAM is not resilient to wrong loop detections, the key of our proposal is the Visual Loop Detector, which operates in two steps. First, a lightweight Siamese Neural Network performs a fast check to discard non loop closing image pairs. Second, a RANSAC based algorithm exhaustively analyzes the remaining image pairs and filters out those that do not close a loop. The accepted image pairs are then introduced as new graph constraints that will be used during the graph optimization. By executing RANSAC only on a previously filtered set of images, the gain in speed is considerable. The experimental results, which evaluate each component separately as well as the whole Visual Graph SLAM system, show the validity of our proposal both in terms of quality of the detected loops, error of the resulting trajectory and execution time.

Paper Citation

in Harvard Style

Burguera A. (2022). Robust Underwater Visual Graph SLAM using a Simanese Neural Network and Robust Image Matching. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 591-598. DOI: 10.5220/0010889100003124

in Bibtex Style

@conference{visapp22,
author={Antoni Burguera},
title={Robust Underwater Visual Graph SLAM using a Simanese Neural Network and Robust Image Matching},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={591-598},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010889100003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Robust Underwater Visual Graph SLAM using a Simanese Neural Network and Robust Image Matching
SN - 978-989-758-555-5
AU - Burguera A.
PY - 2022
SP - 591
EP - 598
DO - 10.5220/0010889100003124
PB - SciTePress