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Position fingerprint localization method based on linear interpolation in robot auditory system

机译:机器人听觉系统中基于线性插值的位置指纹定位方法

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Localization method based on position fingerprint can effectively solve the problems such as high dependence of model and low positioning accuracy in robot auditory system. However, this method requires a large number of reference points to achieve high precision positioning. To solve the problem of low precision under low fingerprint density, a position fingerprint localization method based on linear interpolation is proposed. We use the K Nearest Neighbors (KNN) algorithm to the coarse localization. And then we update the fingerprint database to increase the density of the position fingerprint in the credible area with linear interpolation method. Finally, we use the Weighted K Nearest Neighbors (WKNN) algorithm to accurately estimate the location of the sound source. The experimental results show that under low fingerprint density and high noise environment, the proposed method can improve the localization efficiency effectively and has a strong practicality compared with the conventional robot auditory localization methods.
机译:基于位置指纹的定位方法可以有效解决机器人听觉系统中模型依赖性高,定位精度低的问题。但是,该方法需要大量参考点才能实现高精度定位。为了解决低指纹密度下精度低的问题,提出了一种基于线性插值的位置指纹定位方法。我们使用K最近邻居(KNN)算法进行粗略定位。然后通过线性插值方法更新指纹数据库,以增加可信区域中位置指纹的密度。最后,我们使用加权K最近邻(WKNN)算法来准确估计声源的位置。实验结果表明,在低指纹密度和高噪声环境下,与传统的机器人听觉定位方法相比,该方法可以有效地提高定位效率,具有较强的实用性。

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