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Detection and Elimination of Dynamic Feature Points Based on YOLO and Geometric Constraints

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Abstract

For the problem where dynamic objects in complex environments have a large impact on simultaneous localization and mapping (SLAM) pose estimation and mapping accuracy, you only look once (YOLO) detection method combining optical flow and geometric constraints is proposed to identify and eliminate dynamic feature points. Firstly, the latent dynamic feature points in the environment are detected based on YOLO, and the static and dynamic regions are divided according to motion consistency. Secondly, motion detection and tracking are carried out based on the optical flow method to preliminarily estimate the motion state, and then, re-judgment is performed in combination with geometric constraints to reduce tracking loss and precision reduction caused by false elimination of feature points. Finally, on the basis of eliminating dynamic feature points, static feature points are used for pose estimation to avoid the interference of dynamic feature points on pose estimation. Finally, based on the Technical University Munich (TUM) dataset and SLAM accuracy evaluation indexes, such as absolute trajectory error (ATE), and so on, the proposed method of this paper is experimentally tested and evaluated, and the ATE index of this paper's algorithm is improved by 8.1% and 96.35% compared with ORB-SLAM2 under TUM's low-dynamic sequences and high dynamic sequences, respectively, and the results show that this paper's algorithm has a better accuracy under the dynamic environment.

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Funding

Supported by the National Natural Science Youth Foundation Project (42105143); the Science and Technology Development Fund of Wuxi (N20201011); Vehicle road collaboration application scenario validation (560122034).

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Authors and Affiliations

  1. School of Internet of Things Engineering, Wuxi University, Wuxi, 214000, China

    Jiajia Lu, Yue Tang & Kan Xi

  2. School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210031, China

    Xianwei Wang & Yue Shen

  3. Wuxi Research Institute, Nanjing University of Information Science and Technology, Wuxi, 214000, China

    Weichao Chen

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  1. Jiajia Lu
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  2. Xianwei Wang
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  3. Yue Tang
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  4. Kan Xi
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  5. Yue Shen
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  6. Weichao Chen
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Corresponding author

Correspondence to Yue Tang.

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Lu, J., Wang, X., Tang, Y. et al. Detection and Elimination of Dynamic Feature Points Based on YOLO and Geometric Constraints. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08957-z

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  • DOI: https://doi.org/10.1007/s13369-024-08957-z

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