目的 解决导向辊生产车间物料输送AGV的激光传感器存在的信息复杂度低、重复率高，且在不断迭代重采样过程中极易丢失正确位姿附近粒子造成定位失败等问题。方法 提出一种基于视觉的自适应蒙特卡洛定位算法。建立相机观测模型和自动导引运输车本体运动模型，对观测模型进行去畸变处理，完成相机标定;设计基于视觉的自适应蒙特卡洛算法，获取特征信息，并用词袋模型进行分类，使用激光雷达构建2D栅格地图，采用特征点匹配估计位姿，实现AGV自我精确定位。结果 仿真实验结果表明，本文所提算法与传统自适应蒙特卡洛定位(Adaptive Monte Carlo Localization，AMCL)算法相比，可使机器人更加快速地收敛到精度较高的位姿，具有更好的定位性能。结论 基于视觉的AMCL算法设计，实现了导向辊生产车间机器人的高精度定位，优化了作业流程，提高了生产线系统智能化运行水平，可为其他场景定位应用提供参考。

The work aims to solve the problems of low complexity and high repetition rate of laser sensor information for material delivery AGVs in guiding roller production workshops, which often leads to the loss of particles near the correct pose during iterative resampling and results in positioning failures. A visual-based adaptive Monte Carlo localization algorithm was proposed. A camera observation model and an autonomous guided vehicle (AGV) body motion model were established. The observation model was processed for distortion correction to complete camera calibration. A visual-based adaptive Monte Carlo algorithm was designed to extract feature information and classify it using a bag-of-words model. A 2D map was constructed using laser radar, and feature point matching was utilized to estimate the pose and achieve self-localization of the AGV. The simulation experiments showed that, compared with the traditional Adaptive Monte Carlo Localization (AMCL) algorithm, the proposed localization algorithm enabled the robot to quickly converge to a highly accurate pose and exhibited superior localization performance. The visual-based AMCL algorithm is designed to achieve high-precision positioning for AGVs in guiding roller production workshops, optimizing operational processes and enhancing the level of intelligent operation in production line systems. It can provide reference for locating applications in other scenarios.