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An Error-Matching Exclusion Method for Accelerating Visual SLAM
Feb. 23, 2024, 5:45 a.m. | Shaojie Zhang, Yinghui Wang, Jiaxing Ma, Jinlong Yang, Tao Yan, Liangyi Huang, Mingfeng Wang
cs.CV updates on arXiv.org arxiv.org
Abstract: In Visual SLAM, achieving accurate feature matching consumes a significant amount of time, severely impacting the real-time performance of the system. This paper proposes an accelerated method for Visual SLAM by integrating GMS (Grid-based Motion Statistics) with RANSAC (Random Sample Consensus) for the removal of mismatched features. The approach first utilizes the GMS algorithm to estimate the quantity of matched pairs within the neighborhood and ranks the matches based on their confidence. Subsequently, the Random …
abstract arxiv consensus cs.cv error feature features grid paper performance random real-time sample slam statistics type visual visual slam
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