TS-LCD: Two-Stage Loop-Closure Detection Based on Heterogeneous Data Fusion

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 12; p. 3702
• Main Authors: Jiang, Fangdi, Wang, Wanqiu, You, Hongru, Jiang, Shuhang, Meng, Xin, Kim, Jonghyuk, Wang, Shifeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.06.2024; MDPI
• Subjects: Accuracy; Algorithms; Deep learning; feature extraction; Lasers; Localization; loop-closure detection; Methods; multi-sensor fusion; Neural networks; Semantics; Sensors; timestamp synchronization; loop-closure detection; timestamp synchronization; feature extraction; multi-sensor fusion
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24123702

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.