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Volume 13 Issue 10
Oct.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(10): 2312-2326
K.-L. Chung, Q.-H. Jian, and Y.-Q. Zhang, “Dual codebook- and scene change-based loop closure detection using SVM outlier removal in verification,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 10, pp. 2312–2326, Oct. 2026. doi: 10.1109/JAS.2026.125762
Citation: K.-L. Chung, Q.-H. Jian, and Y.-Q. Zhang, “Dual codebook- and scene change-based loop closure detection using SVM outlier removal in verification,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 10, pp. 2312–2326, Oct. 2026. doi: 10.1109/JAS.2026.125762
shu

Dual Codebook- and Scene Change-Based Loop Closure Detection Using SVM Outlier Removal in Verification

doi: 10.1109/JAS.2026.125762
Funds:  This work was supported by the Ministry of Science and Technology, Taiwan, China (MOST-114-2221-E-011-040-MY3)
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    Abstract
  • Loop closure detection (LCD) is a key component of simultaneous localization and mapping (SLAM) systems, essential for mitigating accumulated errors and ensuring map consistency. This paper presents a novel dual codebook- and scene change-based LCD algorithm, enhanced by a fast support vector machine (SVM)-based outlier removal method during the verification process. Different from using only one static codebook in the existing methods, our first novelty is the proposal of a dual codebook in which the super-features that often appear are saved in the primary codebook, but some rarely used super-features are saved in the secondary codebook for increasing the LCD accuracy. Once the frequency of one codeword in the secondary codebook is larger than the frequency of one codeword in the primary codebook, a codeword-swapping operation is performed to exchange the two concerned codewords between the two codebooks. The second novelty introduces a scene change- and codeword histogram-based method to efficiently retrieve the most relevant candidate images for each query image, significantly reducing computation time. The third novelty presents an SVM-based outlier removal method that accelerates the LCD verification step while preserving high detection accuracy. Based on nine large-scale video sequences, comprehensive experimental results demonstrate that our algorithm outperforms state-of-the-art methods in both LCD accuracy and execution efficiency. Furthermore, an ablation study validates the effectiveness of the three proposed contributions. The source code of our algorithm is available at https://github.com/hankjian7/DC-SC-SVM-LCD.git.

     

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