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Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration

Please always quote using this URN: urn:nbn:de:bvb:20-opus-219988
  • 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, whichMapping 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.show moreshow less

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Metadaten
Author: Shitong Du, Helge A. Lauterbach, Xuyou Li, Girum G. Demisse, Dorit BorrmannORCiD, Andreas NüchterORCiD
URN:urn:nbn:de:bvb:20-opus-219988
Document Type:Journal article
Faculties:Fakultät für Mathematik und Informatik / Institut für Informatik
Language:English
Parent Title (English):Sensors
ISSN:1424-8220
Year of Completion:2020
Volume:20
Issue:23
Article Number:6918
Source:Sensors 2020, 20(23), 6918; https://doi.org/10.3390/s20236918
DOI:https://doi.org/10.3390/s20236918
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Tag:continuous-time SLAM; deformation-based method; mapping; time calibration
Release Date:2021/03/16
Date of first Publication:2020/12/03
Open-Access-Publikationsfonds / Förderzeitraum 2020
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International