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AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM

In robot localisation and mapping, outliers are unavoidable when loop-closure measurements are taken into account. A single false-positive loop-closure can have a very negative impact on SLAM problems causing an inferior trajectory to be produced or even for the optimisation to fail entirely. To add...

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Autores principales: Ramezani, Milad, Mattamala, Matias, Fallon, Maurice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010720/
https://www.ncbi.nlm.nih.gov/pubmed/35433840
http://dx.doi.org/10.3389/frobt.2022.789444
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author Ramezani, Milad
Mattamala, Matias
Fallon, Maurice
author_facet Ramezani, Milad
Mattamala, Matias
Fallon, Maurice
author_sort Ramezani, Milad
collection PubMed
description In robot localisation and mapping, outliers are unavoidable when loop-closure measurements are taken into account. A single false-positive loop-closure can have a very negative impact on SLAM problems causing an inferior trajectory to be produced or even for the optimisation to fail entirely. To address this issue, popular existing approaches define a hard switch for each loop-closure constraint. This paper presents AEROS, a novel approach to adaptively solve a robust least-squares minimisation problem by adding just a single extra latent parameter. It can be used in the back-end component of the SLAM system to enable generalised robust cost minimisation by simultaneously estimating the continuous latent parameter along with the set of sensor poses in a single joint optimisation. This leads to a very closely curve fitting on the distribution of the residuals, thereby reducing the effect of outliers. Additionally, we formulate the robust optimisation problem using standard Gaussian factors so that it can be solved by direct application of popular incremental estimation approaches such as iSAM. Experimental results on publicly available synthetic datasets and real LiDAR-SLAM datasets collected from the 2D and 3D LiDAR systems show the competitiveness of our approach with the state-of-the-art techniques and its superiority on real world scenarios.
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spelling pubmed-90107202022-04-16 AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM Ramezani, Milad Mattamala, Matias Fallon, Maurice Front Robot AI Robotics and AI In robot localisation and mapping, outliers are unavoidable when loop-closure measurements are taken into account. A single false-positive loop-closure can have a very negative impact on SLAM problems causing an inferior trajectory to be produced or even for the optimisation to fail entirely. To address this issue, popular existing approaches define a hard switch for each loop-closure constraint. This paper presents AEROS, a novel approach to adaptively solve a robust least-squares minimisation problem by adding just a single extra latent parameter. It can be used in the back-end component of the SLAM system to enable generalised robust cost minimisation by simultaneously estimating the continuous latent parameter along with the set of sensor poses in a single joint optimisation. This leads to a very closely curve fitting on the distribution of the residuals, thereby reducing the effect of outliers. Additionally, we formulate the robust optimisation problem using standard Gaussian factors so that it can be solved by direct application of popular incremental estimation approaches such as iSAM. Experimental results on publicly available synthetic datasets and real LiDAR-SLAM datasets collected from the 2D and 3D LiDAR systems show the competitiveness of our approach with the state-of-the-art techniques and its superiority on real world scenarios. Frontiers Media S.A. 2022-04-01 /pmc/articles/PMC9010720/ /pubmed/35433840 http://dx.doi.org/10.3389/frobt.2022.789444 Text en Copyright © 2022 Ramezani, Mattamala and Fallon. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Ramezani, Milad
Mattamala, Matias
Fallon, Maurice
AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM
title AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM
title_full AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM
title_fullStr AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM
title_full_unstemmed AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM
title_short AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM
title_sort aeros: adaptive robust least-squares for graph-based slam
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010720/
https://www.ncbi.nlm.nih.gov/pubmed/35433840
http://dx.doi.org/10.3389/frobt.2022.789444
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