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IEEE/CAA Journal of Automatica Sinica

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J. Y. Ma, K. N. Zhang, and J. J. Jiang, “Loop closure detection via locality preserving matching with global consensus,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 1–16, Feb. 2023. doi: 10.1109/JAS.2022.105926
Citation: J. Y. Ma, K. N. Zhang, and J. J. Jiang, “Loop closure detection via locality preserving matching with global consensus,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 1–16, Feb. 2023. doi: 10.1109/JAS.2022.105926

Loop Closure Detection via Locality Preserving Matching With Global Consensus

doi: 10.1109/JAS.2022.105926
Funds:  This work was supported by the Key Research and Development Program of Hubei Province (2020BAB113)
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  • A critical component of visual simultaneous localization and mapping is loop closure detection (LCD), an operation judging whether a robot has come to a pre-visited area. Concretely, given a query image (i.e., the latest view observed by the robot), it proceeds by first exploring images with similar semantic information, followed by solving the relative relationship between candidate pairs in the 3D space. In this work, a novel appearance-based LCD system is proposed. Specifically, candidate frame selection is conducted via the combination of Super-features and aggregated selective match kernel (ASMK). We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task. It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance. To dig up consistent geometry between image pairs during loop closure verification, we propose a simple yet surprisingly effective feature matching algorithm, termed locality preserving matching with global consensus (LPM-GC). The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs, where a global constraint is further designed to effectively remove false correspondences in challenging sceneries, e.g., containing numerous repetitive structures. Meanwhile, we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds. The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets. Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks. We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.

     

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  • 1 In the introduction of “Super-features”, the term “query” refers to the input of the Transformer architecture, which has different meaning with that in image retrieval and LCD.
    2 https://github.com/jenicek/asmk
    3 https://github.com/axelBarroso/Key.Net-Pytorch
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