Automated Guided Vehicle (AGV) indoor autonomous cargo handling and commodity transportation are inseparable from AGV autonomous navigation, and positioning and navigation in an unknown environment are the keys of AGV technology. In this paper, the extended Kalman filter algorithm is used to match the sensor observations with the existing features in the map to determine the accurate positioning of the AGV. This paper proposes an improved joint compatibility branch and bound (JCBB) method to divide the data and then randomly extract part of the data in the divided data set, thereby reducing the data association space; then, the JCBB algorithm is used to perform data association and finally merge the associated data. This method can solve the problem of the increased computational complexity of JCBB when the amount of data to be matched is large to achieve the effect of increasing the correlation speed and not reducing the accuracy rate, thereby ensuring the real-time positioning of the AGV.

中文翻译：

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基于联合兼容分支定界的改进型AGV实时定位模型

自动导引车（AGV）的室内自动货物搬运和商品运输与AGV自主导航密不可分，在未知环境中进行定位和导航是AGV技术的关键。在本文中，扩展的卡尔曼滤波算法用于将传感器的观测值与地图中的现有特征进行匹配，以确定AGV的精确定位。提出了一种改进的联合兼容性分支定界法（JCBB），对数据进行划分，然后从划分后的数据集中随机抽取部分数据，从而减少了数据关联空间。然后，使用JCBB算法执行数据关联并最终合并关联的数据。