Modeling a priori Unknown Environments: Place Recognition with Optical Flow Fingerprints

Zachary Mueller, Sotirios Diamantas

2021

Abstract

In this research we present a novel method for place recognition that relies on optical flow fingerprints of features. We make no assumptions about the properties of features or the environment such as color, shape, and size, as we approach the problem parsimoniously with a single camera mounted on a robot. In the training phase of our algorithm an accurate camera model is utilized to model and simulate the optical flow vector magnitudes with respect to velocity and distance to features. A lognormal distribution function, that is the result of this observation, is used as an input during the testing phase that is taking place with real sensors and features extracted using Lucas-Kanade optical flow algorithm. With this approach we have managed to bridge the gap between simulation and real-world environments by transferring the output of simulated training data sets to real testing environments. In addition, our method is highly adaptable to different types of sensors and environments. Our algorithm is evaluated both in indoor and outdoor environments where a robot revisits places from different poses and velocities demonstrating that modeling an unknown environment using optical flow properties is feasible yet efficient.

Paper Citation

in Harvard Style

Mueller Z. and Diamantas S. (2021). Modeling a priori Unknown Environments: Place Recognition with Optical Flow Fingerprints. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 793-800. DOI: 10.5220/0010271007930800

in Bibtex Style

@conference{visapp21,
author={Zachary Mueller and Sotirios Diamantas},
title={Modeling a priori Unknown Environments: Place Recognition with Optical Flow Fingerprints},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={793-800},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010271007930800},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Modeling a priori Unknown Environments: Place Recognition with Optical Flow Fingerprints
SN - 978-989-758-488-6
AU - Mueller Z.
AU - Diamantas S.
PY - 2021
SP - 793
EP - 800
DO - 10.5220/0010271007930800
PB - SciTePress