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GGC-SLAM: a VSLAM system based on predicted static probability of feature points in dynamic environments

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Abstract

Most existing Visual Simultaneous Localization and Map** (VSLAM) systems heavily rely on the assumption of a static world. However, in real-world scenarios, moving objects often reduce the accuracy and robustness of these systems. To mitigate the impact of moving objects on SLAM system performance, this paper introduces GGC-SLAM, a VSLAM system designed for dynamic environments based on the static probability of predictive feature points. Building on the foundation of ORB-SLAM2, this system incorporates a lightweight object detection thread for acquiring semantic information. It also integrates the Grid-based Motion Statistics (GMS) and Graph-Cut RANdom SAmple Consensus (GC-RANSAC) algorithms to enhance the speed and precision of fundamental matrix computation. The system preliminarily predicts the static probability of feature points by combining semantic information with epipolar constraints, then refines the differentiation between dynamic and static feature points using the initial probability information and a Conditional Random Field, ultimately excluding feature points deemed dynamic. We conducted evaluations using the TUM public dataset and in real-world environments, and the results show that GGC-SLAM can effectively handle dynamic feature points in dynamic scenes. While ensuring real-time operation, it demonstrates more accurate localization compared to other advanced dynamic SLAM systems. Particularly in high-dynamic scenes, our system’s average absolute trajectory error is reduced by 95.71% compared to ORB-SLAM2.

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Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, Heilongjiang, China

    Qian Sun, Wa Liu, Jun**g Zou, Ziqiang Xu & Yibing Li

  2. Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin, 150001, Heilongjiang, China

    Qian Sun, Wa Liu, Jun**g Zou, Ziqiang Xu & Yibing Li

Authors
  1. Qian Sun
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  2. Wa Liu
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  3. Jun**g Zou
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  4. Ziqiang Xu
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  5. Yibing Li
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Contributions

Q.S., W.L. and Y.B.L. conceived the idea of the study; Q.S., W.L. and Y.B.L. did the analyses; Q.S., W.L., J.J.Z., Z.Q.X. and Y.B.L. interpreted the results; all authors contributed to the writing and revisions.

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Correspondence to Yibing Li.

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Sun, Q., Liu, W., Zou, J. et al. GGC-SLAM: a VSLAM system based on predicted static probability of feature points in dynamic environments. SIViP (2024). https://doi.org/10.1007/s11760-024-03375-y

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