视觉SLAM中基于误匹配风险预测的特征选择

Erroneous Matching Risk Prediction Based Feature Selection for Visual SLAM

  • 摘要: 针对视觉SLAM(同时定位与建图)问题,提出了一种预测误匹配风险最小化的特征选择方法.该方法采用预测误匹配风险来衡量新检测到的特征对未来特征匹配过程的影响,然后采用多级排序的方法优先选择误匹配风险小且重现率高的候选特征进行初始化.该方法能根据系统状态估计不确定度的强弱自适应地选择不易被误匹配的特征,从而保证了SLAM算法的收敛性和一致性.在实际单目视觉SLAM系统上的对比实验表明,本文方法在降低特征误匹配率和保证SLAM结果的正确性等方面具有明显优势.

     

    Abstract: A predictive erroneous matching risk minimization based feature selection method is proposed for visual SLAM (simultaneous localization and mapping).It uses predictive erroneous matching risk to measure the influence of newly detected features on the oncoming feature matching process.Then,based on a multi-layer ranking method,the new features with lower erroneous matching risk and higher repeatability are selected for initialization with priority.This method can adaptively select good features that are not prone to be erroneously matched according to the uncertainty in state estimation. Therefore,the convergency and consistency of the SLAM algorithm can be ensured.The comparative experiment results on a mono-SLAM system validate that the proposed method has significant advantages over the existing methods in reducing erroneous matching rate and ensuring the correctness of state estimation.

     

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