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提出了一种基于激光数据配准的移动机器人自定位方法。该方法避免了对激光数据进行特征提取以及点对点的对应,仅以预处理后激光数据的核密度估计作为定位依据,以核相关方法作为比较相邻两组激光数据相似性的度量准则,并在此基础上建立以旋转平移向量为参数的自定位目标函数。最后采用BFGS拟牛顿方法对目标函数进行寻优,最终实现移动机器人的自定位。对180度激光数据的仿真实验结果证明了该方法的有效性。
A self-localization method of mobile robot based on laser data registration is proposed. This method avoids feature extraction and point-to-point correspondence of laser data. Only the kernel density estimation of laser data after pretreatment is taken as a basis for positioning. The nuclear correlation method is used as a measure to compare the similarity of two adjacent laser data. Based on this, a self-locating objective function based on the rotational translation vector is established. Finally, using the BFGS quasi-Newton method to optimize the objective function, and finally realize the self-localization of the mobile robot. The experimental results on the 180-degree laser data demonstrate the effectiveness of the proposed method.