Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-aliased Indoor Environments

Satyajit Tourani, Dhagash Desai, Udit Parihar, Sourav Garg, Ravi Sarvadevabhatla, Michael Milford, K. Krishna

Abstract

Significant recent advances have been made in Visual Place Recognition (VPR), feature correspondence and localization due to deep-learning-based methods. However, existing approaches tend to address, partially or fully, only one of two key challenges: viewpoint change and perceptual aliasing. In this paper, we present novel research that simultaneously addresses both challenges by combining deep-learnt features with geometric transformations based on domain knowledge about navigation on a ground-plane, without specialized hardware (e.g. downwards facing cameras, etc.). In particular, our integration of VPR with SLAM by leveraging the robustness of deep-learnt features and our homography-based extreme viewpoint invariance significantly boosts the performance of VPR, feature correspondence and pose graph sub-modules of the SLAM pipeline. We demonstrate a localization system capable of state-of-the-art performance despite perceptual aliasing and extreme 180-degree-rotated viewpoint change in a range of real-world and simulated experiments. Our system is able to achieve early loop closures that prevent significant drifts in SLAM trajectories.

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Paper Citation


in Harvard Style

Tourani S., Desai D., Parihar U., Garg S., Sarvadevabhatla R., Milford M. and Krishna K. (2021). Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-aliased Indoor Environments.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 409-416. DOI: 10.5220/0010230804090416


in Bibtex Style

@conference{visapp21,
author={Satyajit Tourani and Dhagash Desai and Udit Parihar and Sourav Garg and Ravi Sarvadevabhatla and Michael Milford and K. Krishna},
title={Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-aliased Indoor Environments},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={409-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010230804090416},
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 - Volume 4: VISAPP,
TI - Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-aliased Indoor Environments
SN - 978-989-758-488-6
AU - Tourani S.
AU - Desai D.
AU - Parihar U.
AU - Garg S.
AU - Sarvadevabhatla R.
AU - Milford M.
AU - Krishna K.
PY - 2021
SP - 409
EP - 416
DO - 10.5220/0010230804090416