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DenseFilter: Feature Correspondence Filter Based on Dense Networks for VSLAM

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Abstract

Feature matching can establish reliable points correspondences between two images, which plays a vital role in indirect Visual Simultaneous Localization and Mapping (VSLAM). The VSLAM system performs data association between image frames through feature matching. There are a large number of incorrect correspondences in the unfiltered matching results, and the accuracy of data association will directly determine the localization performance. This paper proposes a convolutional neural network with dense connectivity to eliminate the outliers in the matching results. It repeatedly concatenates low-level and high-level features into a dense feature map, which allows both local and global information to be used for mismatch removal. Furthermore, we introduce an outlier rejection module based on our network into the classic monocular visual odometry (VO) pipeline, in order to enhance the reliability of data association. Experiments on several benchmark datasets show that our method achieves favorable performance against other algorithms, especially under challenging scenes, such as low texture. Also, we verify the performance of our system on a mobile robot in real-world localization task.

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Funding

This work was supported by Department of science and technology of Guangdong Province (No:2021B01420003) and Department of science and technology of Foshan Nanhai District (No: 201811020005).

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, 510641, Guangzhou, China

    Jinze Xu, Lingfeng Su, Feng Ye, Kuo Li & Yizong Lai

Authors
  1. Jinze Xu
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  2. Lingfeng Su
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  3. Feng Ye
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  4. Kuo Li
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  5. Yizong Lai
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Contributions

All authors contributed to the methodology conception and design. Data collection and analysis were performed by Jinze Xu and Lingfeng Su. Jinze Xu, Lingfeng Su and Feng Ye wrote the manuscript. Kuo Li and Yizong Lai reviewed and approved the manuscript.

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Correspondence to Feng Ye.

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Xu, J., Su, L., Ye, F. et al. DenseFilter: Feature Correspondence Filter Based on Dense Networks for VSLAM. J Intell Robot Syst 106, 18 (2022). https://doi.org/10.1007/s10846-022-01735-9

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