## 1 Introduction  
An automated Guided Vehicle is a robot used for equipment handling and automatic assembly [1]. With the development of industry and the increase in the cost of human resources, material handling in industrial production has been gradually replaced by intelligent AGVs [2], which are equipped with various kinds of photoelectric sensors to network and interconnect, and issue scheduling instructions to realize the intelligent material handling system. According to the different navigation methods, AGVs can be divided into QR code, magnetic, inertial, laser, etc. [3]. When AGVs convey material along a planned route, their surroundings and ground cleanliness partially impact most navigation methods [4]. As we all know, QR code navigation requires a neat and clean floor, and the QR code must be protected to a large extent [5]. On the contrary, SLAM navigation does not require ground road conditions to build a map but has a strong need for the surrounding environment [6]. For the above situation, the navigation method is established as SLAM fused QR code navigation, and the improved AMCL positioning algorithm is used to match the QR code information to improve the positioning accuracy. AGV navigation has been a fundamental problem for logistics robots. The key to this is the combination of algorithms with various navigation and optimization methods. [PERSON] et al. [7] corrected the cumulative error in attitude estimation by combining the internal coded sensor data with the remaining external QR code data. [PERSON] et al. [8] used RGB cameras and QR codes for mapping and localization, eliminating permanent localization errors, but the light source affects the actual localization. [PERSON] [9] used EKF for the data tracking of QR codes and the experiments proved that the positioning accuracy is improved but the quality of the QR code is required and regular maintenance is needed. [PERSON]'s [10] use of QR codes for AGV navigation has been shown through experiments to reduce costs and improve positioning accuracy. Still, the system uses machine vision to recognize QR codes and therefore has an impact on the scanning time as well as precision. Adaptive Monte Carlo localization is a very effective solution for localizing the robot in a given environment [11]. [PERSON], [PERSON] et al. [12] proposed an improved EKF intelligent algorithm to reduce and eliminate the deviation in the fusion of various sensor data. When in a highly similar working environment, the positioning accuracy, stability, and data processing will be significantly affected [13]. The parameter changes in the AMCL positioning algorithm also impact the algorithm response and actual positioning [14; 15]. SLAM [16] is the abbreviation for \"simultaneous localization and mapping,\" mainly used to solve the problem of localization and map construction when a robot moves in an unknown environment. It consists of four main parts: front-end scanning and matching, back-end optimization, closed-loop detection, and map construction [17]. Gmapping is a SLAM algorithm based on laser sensors, which has high map building and positioning accuracy, and is able to build accurate maps in real time and achieve more accurate positioning [18]. AMCL localization algorithms are widely used because they are highly flexible, adaptive and can handle noise and uncertainty [19]. However, in localization, AMCL is highly dependent on the accuracy of the sensors, which will affect the accuracy of the localization if the data fails. In this paper, an enhanced AMCL based on QR code information is proposed. The improved AMCL algorithm is able to automatically adjust the weights and distributions of the particles according to the estimation error through an adaptive sampling strategy, which improves the accuracy and robustness of localization. Accurate localization is still achieved in the presence of dynamic environment and sensor errors. To meet the requirements of the real-time and positioning accuracy of visual navigation, this paper uses the QR code as a landmark.