Hybrid Monocular SLAM Using Double Window Optimization

Research output: Contribution to journalArticleResearchpeer review

Authors

View graph of relations

Details

Original languageEnglish
Article number9392262
Pages (from-to)4899-4906
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume6
Issue number3
Publication statusPublished - 31 Mar 2021

Abstract

This letter presents a hybrid framework, both in front-end and back-end, for monocular simultaneous localization and mapping (SLAM), capable of utilizing the robustness of feature matching and the accuracy of direct alignment. In the front-end, the feature-based method is first used for coarse pose estimation that is subsequently taken by the direct alignment module as initialization for further refinement. In the back-end, a double window structure is constructed based on the maintained semi-dense map and the sparse feature map, of which the states are optimized via a multi-layer optimization scheme based on the reprojection constraints and the relative pose constraints. Our evaluation on several public datasets demonstrates that this hybrid design retains the superior resilience to scene variations of salient features, and achieves better tracking accuracy due to the integration of the direct modules, leading to a comparable performance with the state-of-the-arts.

Keywords

    double window optimization, mapping, SLAM

ASJC Scopus subject areas

Cite this

Hybrid Monocular SLAM Using Double Window Optimization. / Luo, Hang; Pape, Christian; Reithmeier, Eduard.
In: IEEE Robotics and Automation Letters, Vol. 6, No. 3, 9392262, 31.03.2021, p. 4899-4906.

Research output: Contribution to journalArticleResearchpeer review

Luo, H, Pape, C & Reithmeier, E 2021, 'Hybrid Monocular SLAM Using Double Window Optimization', IEEE Robotics and Automation Letters, vol. 6, no. 3, 9392262, pp. 4899-4906. https://doi.org/10.1109/LRA.2021.3070298
Luo, H., Pape, C., & Reithmeier, E. (2021). Hybrid Monocular SLAM Using Double Window Optimization. IEEE Robotics and Automation Letters, 6(3), 4899-4906. Article 9392262. https://doi.org/10.1109/LRA.2021.3070298
Luo H, Pape C, Reithmeier E. Hybrid Monocular SLAM Using Double Window Optimization. IEEE Robotics and Automation Letters. 2021 Mar 31;6(3):4899-4906. 9392262. doi: 10.1109/LRA.2021.3070298
Luo, Hang ; Pape, Christian ; Reithmeier, Eduard. / Hybrid Monocular SLAM Using Double Window Optimization. In: IEEE Robotics and Automation Letters. 2021 ; Vol. 6, No. 3. pp. 4899-4906.
Download
@article{c2f73dc2dfa546d0a84aa1701c6dc23b,
title = "Hybrid Monocular SLAM Using Double Window Optimization",
abstract = "This letter presents a hybrid framework, both in front-end and back-end, for monocular simultaneous localization and mapping (SLAM), capable of utilizing the robustness of feature matching and the accuracy of direct alignment. In the front-end, the feature-based method is first used for coarse pose estimation that is subsequently taken by the direct alignment module as initialization for further refinement. In the back-end, a double window structure is constructed based on the maintained semi-dense map and the sparse feature map, of which the states are optimized via a multi-layer optimization scheme based on the reprojection constraints and the relative pose constraints. Our evaluation on several public datasets demonstrates that this hybrid design retains the superior resilience to scene variations of salient features, and achieves better tracking accuracy due to the integration of the direct modules, leading to a comparable performance with the state-of-the-arts.",
keywords = "double window optimization, mapping, SLAM",
author = "Hang Luo and Christian Pape and Eduard Reithmeier",
note = "Funding Information: Manuscript received December 7, 2020; accepted March 21, 2021. Date of publication March 31, 2021; date of current version April 20, 2021. This letter was recommended for publication by Associate Editor Y. Latif and Editor J. Civera upon evaluation of the reviewers{\textquoteright} comments. This work was supported by China Scholarship Council. (Corresponding author: Hang Luo.) The authors are with the Institute of Measurement and Automatic Control, Faculty of Mechanical Engineering, Leibniz University Hannover, 30167 Hanover, Germany (e-mail: luo@imr.uni-hannover.de; Christian.Pape@imr.uni-hannover.de; reithmeier@imr.uni-hannover.de). Digital Object Identifier 10.1109/LRA.2021.3070298",
year = "2021",
month = mar,
day = "31",
doi = "10.1109/LRA.2021.3070298",
language = "English",
volume = "6",
pages = "4899--4906",
number = "3",

}

Download

TY - JOUR

T1 - Hybrid Monocular SLAM Using Double Window Optimization

AU - Luo, Hang

AU - Pape, Christian

AU - Reithmeier, Eduard

N1 - Funding Information: Manuscript received December 7, 2020; accepted March 21, 2021. Date of publication March 31, 2021; date of current version April 20, 2021. This letter was recommended for publication by Associate Editor Y. Latif and Editor J. Civera upon evaluation of the reviewers’ comments. This work was supported by China Scholarship Council. (Corresponding author: Hang Luo.) The authors are with the Institute of Measurement and Automatic Control, Faculty of Mechanical Engineering, Leibniz University Hannover, 30167 Hanover, Germany (e-mail: luo@imr.uni-hannover.de; Christian.Pape@imr.uni-hannover.de; reithmeier@imr.uni-hannover.de). Digital Object Identifier 10.1109/LRA.2021.3070298

PY - 2021/3/31

Y1 - 2021/3/31

N2 - This letter presents a hybrid framework, both in front-end and back-end, for monocular simultaneous localization and mapping (SLAM), capable of utilizing the robustness of feature matching and the accuracy of direct alignment. In the front-end, the feature-based method is first used for coarse pose estimation that is subsequently taken by the direct alignment module as initialization for further refinement. In the back-end, a double window structure is constructed based on the maintained semi-dense map and the sparse feature map, of which the states are optimized via a multi-layer optimization scheme based on the reprojection constraints and the relative pose constraints. Our evaluation on several public datasets demonstrates that this hybrid design retains the superior resilience to scene variations of salient features, and achieves better tracking accuracy due to the integration of the direct modules, leading to a comparable performance with the state-of-the-arts.

AB - This letter presents a hybrid framework, both in front-end and back-end, for monocular simultaneous localization and mapping (SLAM), capable of utilizing the robustness of feature matching and the accuracy of direct alignment. In the front-end, the feature-based method is first used for coarse pose estimation that is subsequently taken by the direct alignment module as initialization for further refinement. In the back-end, a double window structure is constructed based on the maintained semi-dense map and the sparse feature map, of which the states are optimized via a multi-layer optimization scheme based on the reprojection constraints and the relative pose constraints. Our evaluation on several public datasets demonstrates that this hybrid design retains the superior resilience to scene variations of salient features, and achieves better tracking accuracy due to the integration of the direct modules, leading to a comparable performance with the state-of-the-arts.

KW - double window optimization

KW - mapping

KW - SLAM

UR - http://www.scopus.com/inward/record.url?scp=85103793475&partnerID=8YFLogxK

U2 - 10.1109/LRA.2021.3070298

DO - 10.1109/LRA.2021.3070298

M3 - Article

AN - SCOPUS:85103793475

VL - 6

SP - 4899

EP - 4906

JO - IEEE Robotics and Automation Letters

JF - IEEE Robotics and Automation Letters

SN - 2377-3766

IS - 3

M1 - 9392262

ER -

By the same author(s)