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Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration
Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped wit...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730925/ https://www.ncbi.nlm.nih.gov/pubmed/33287306 http://dx.doi.org/10.3390/s20236918 |
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author | Du, Shitong Lauterbach, Helge A. Li, Xuyou Demisse, Girum G. Borrmann, Dorit Nüchter, Andreas |
author_facet | Du, Shitong Lauterbach, Helge A. Li, Xuyou Demisse, Girum G. Borrmann, Dorit Nüchter, Andreas |
author_sort | Du, Shitong |
collection | PubMed |
description | Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences. |
format | Online Article Text |
id | pubmed-7730925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77309252020-12-12 Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration Du, Shitong Lauterbach, Helge A. Li, Xuyou Demisse, Girum G. Borrmann, Dorit Nüchter, Andreas Sensors (Basel) Article Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences. MDPI 2020-12-03 /pmc/articles/PMC7730925/ /pubmed/33287306 http://dx.doi.org/10.3390/s20236918 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Du, Shitong Lauterbach, Helge A. Li, Xuyou Demisse, Girum G. Borrmann, Dorit Nüchter, Andreas Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration |
title | Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration |
title_full | Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration |
title_fullStr | Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration |
title_full_unstemmed | Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration |
title_short | Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration |
title_sort | curvefusion—a method for combining estimated trajectories with applications to slam and time-calibration |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730925/ https://www.ncbi.nlm.nih.gov/pubmed/33287306 http://dx.doi.org/10.3390/s20236918 |
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