基于激光数据配准的移动机器人自定位

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。

Mobile Robot Self-localization Based on Laser Data Registration

A self-localization method based on laser data registration for mobile robot was proposed. This method avoided the feature extraction of laser data and the corresponding of point to point. It only used the kernel density estimation of the laser data after preprocessing as the localization basis. The kernel correlation was used as the similarity measure of the adjacent laser data and the self-localization object function with the parameter of rotation-translation vector was constructed based on it. Last the object function was optimized by BFGS quasi Newton method,and the self-localization of the mobile robot was realized. Simulation experiment results demonstrate that this method is effective and can be used in any environment.