TS-LCD: Two-Stage Loop-Closure Detection Based on Heterogeneous Data Fusion
Loop-closure detection plays a pivotal role in simultaneous localization and mapping (SLAM). It serves to minimize cumulative errors and ensure the overall consistency of the generated map. This paper introduces a multi-sensor fusion-based loop-closure detection scheme (TS-LCD) to address the challe...
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| Published in | Sensors (Basel, Switzerland) Vol. 24; no. 12; p. 3702 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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MDPI AG
07.06.2024
MDPI |
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| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s24123702 |
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| Abstract | Loop-closure detection plays a pivotal role in simultaneous localization and mapping (SLAM). It serves to minimize cumulative errors and ensure the overall consistency of the generated map. This paper introduces a multi-sensor fusion-based loop-closure detection scheme (TS-LCD) to address the challenges of low robustness and inaccurate loop-closure detection encountered in single-sensor systems under varying lighting conditions and structurally similar environments. Our method comprises two innovative components: a timestamp synchronization method based on data processing and interpolation, and a two-order loop-closure detection scheme based on the fusion validation of visual and laser loops. Experimental results on the publicly available KITTI dataset reveal that the proposed method outperforms baseline algorithms, achieving a significant average reduction of 2.76% in the trajectory error (TE) and a notable decrease of 1.381 m per 100 m in the relative error (RE). Furthermore, it boosts loop-closure detection efficiency by an average of 15.5%, thereby effectively enhancing the positioning accuracy of odometry. |
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| AbstractList | Loop-closure detection plays a pivotal role in simultaneous localization and mapping (SLAM). It serves to minimize cumulative errors and ensure the overall consistency of the generated map. This paper introduces a multi-sensor fusion-based loop-closure detection scheme (TS-LCD) to address the challenges of low robustness and inaccurate loop-closure detection encountered in single-sensor systems under varying lighting conditions and structurally similar environments. Our method comprises two innovative components: a timestamp synchronization method based on data processing and interpolation, and a two-order loop-closure detection scheme based on the fusion validation of visual and laser loops. Experimental results on the publicly available KITTI dataset reveal that the proposed method outperforms baseline algorithms, achieving a significant average reduction of 2.76% in the trajectory error (TE) and a notable decrease of 1.381 m per 100 m in the relative error (RE). Furthermore, it boosts loop-closure detection efficiency by an average of 15.5%, thereby effectively enhancing the positioning accuracy of odometry. Loop-closure detection plays a pivotal role in simultaneous localization and mapping (SLAM). It serves to minimize cumulative errors and ensure the overall consistency of the generated map. This paper introduces a multi-sensor fusion-based loop-closure detection scheme (TS-LCD) to address the challenges of low robustness and inaccurate loop-closure detection encountered in single-sensor systems under varying lighting conditions and structurally similar environments. Our method comprises two innovative components: a timestamp synchronization method based on data processing and interpolation, and a two-order loop-closure detection scheme based on the fusion validation of visual and laser loops. Experimental results on the publicly available KITTI dataset reveal that the proposed method outperforms baseline algorithms, achieving a significant average reduction of 2.76% in the trajectory error (TE) and a notable decrease of 1.381 m per 100 m in the relative error (RE). Furthermore, it boosts loop-closure detection efficiency by an average of 15.5%, thereby effectively enhancing the positioning accuracy of odometry.Loop-closure detection plays a pivotal role in simultaneous localization and mapping (SLAM). It serves to minimize cumulative errors and ensure the overall consistency of the generated map. This paper introduces a multi-sensor fusion-based loop-closure detection scheme (TS-LCD) to address the challenges of low robustness and inaccurate loop-closure detection encountered in single-sensor systems under varying lighting conditions and structurally similar environments. Our method comprises two innovative components: a timestamp synchronization method based on data processing and interpolation, and a two-order loop-closure detection scheme based on the fusion validation of visual and laser loops. Experimental results on the publicly available KITTI dataset reveal that the proposed method outperforms baseline algorithms, achieving a significant average reduction of 2.76% in the trajectory error (TE) and a notable decrease of 1.381 m per 100 m in the relative error (RE). Furthermore, it boosts loop-closure detection efficiency by an average of 15.5%, thereby effectively enhancing the positioning accuracy of odometry. |
| Author | Meng, Xin Wang, Shifeng Jiang, Shuhang Kim, Jonghyuk Wang, Wanqiu Jiang, Fangdi You, Hongru |
| AuthorAffiliation | 1 School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China; jiangfangdi@outlook.com (F.J.); wangwanqiu0524@163.com (W.W.); youbinglian888@126.com (H.Y.); jiang_shuhang@163.com (S.J.); mengxin126536@163.com (X.M.) 3 Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528400, China 2 Center of Excellence in Cybercrimes and Digital Forensics, Naif Arab University for Security Sciences, Riyadh 11452, Saudi Arabia; jkim@nauss.edu.sa |
| AuthorAffiliation_xml | – name: 1 School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China; jiangfangdi@outlook.com (F.J.); wangwanqiu0524@163.com (W.W.); youbinglian888@126.com (H.Y.); jiang_shuhang@163.com (S.J.); mengxin126536@163.com (X.M.) – name: 3 Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528400, China – name: 2 Center of Excellence in Cybercrimes and Digital Forensics, Naif Arab University for Security Sciences, Riyadh 11452, Saudi Arabia; jkim@nauss.edu.sa |
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| Keywords | loop-closure detection timestamp synchronization feature extraction multi-sensor fusion |
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| Snippet | Loop-closure detection plays a pivotal role in simultaneous localization and mapping (SLAM). It serves to minimize cumulative errors and ensure the overall... |
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| SubjectTerms | Accuracy Algorithms Deep learning feature extraction Lasers Localization loop-closure detection Methods multi-sensor fusion Neural networks Semantics Sensors timestamp synchronization |
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| Title | TS-LCD: Two-Stage Loop-Closure Detection Based on Heterogeneous Data Fusion |
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