目的 移动智能体在执行同步定位与地图构建(Simultaneous Localization and Mapping，SLAM)的复杂任务时，动态物体的干扰会导致特征点间的关联减弱，系统定位精度下降，为此提出一种面向室内动态场景下基于YOLOv5和几何约束的视觉SLAM算法。方法 首先，以YOLOv5s为基础，将原有的CSPDarknet主干网络替换成轻量级的MobileNetV3网络，可以减少参数、加快运行速度，同时与ORB-SLAM2系统相结合，在提取ORB特征点的同时获取语义信息，并剔除先验的动态特征点。然后，结合光流法和对极几何约束对可能残存的动态特征点进一步剔除。最后，仅用静态特征点对相机位姿进行估计。结果 在TUM数据集上的实验结果表明，与ORB-SLAM2相比，在高动态序列下的ATE和RPE都减少了90%以上，与DS-SLAM、Dyna-SLAM同类型系统相比，在保证定位精度和鲁棒性的同时，跟踪线程中处理一帧图像平均只需28.26 ms。结论 该算法能够有效降低动态物体对实时SLAM过程造成的干扰，为实现更加智能化、自动化的包装流程提供了可能。

When mobile intelligence agent performs the complex task of Simultaneous Localization And Mapping (SLAM), the interference of dynamic objects will weaken the correlation between feature points and the degradation of the system's localization accuracy. In this regard, the work aims to propose a visual SLAM algorithm based on YOLOv5 and geometric constraints for indoor dynamic scenes. First, based on YOLOv5s, the original CSPDarknet backbone network was replaced by a lightweight MobileNetV3 network, which could reduce parameters and speed up operation, and at the same time, it was combined with the ORB-SLAM2 system to obtain semantic information and eliminate a priori dynamic feature points while extracting ORB feature points. Then, the possible residual dynamic feature points were further culled by combining the optical flow method and epipolar geometric constraints. Finally, only static feature points were used for camera position estimation. Experimental results on the TUM data set showed that both ATE and RPE were reduced by more than 90% on average under high dynamic sequences compared with ORB-SLAM2, and the processing of one frame in the tracking thread took only 28.26 ms on average compared with the same type of systems of DS-SLAM and Dyna-SLAM, while guaranteeing localization accuracy and robustness. The algorithm can effectively reduce the interference caused by dynamic objects to the real-time SLAM process. It provides a possibility for more intelligent and automatic packaging process.