针对视觉同步定位与建图算法在遮挡情况下易受到干扰而导致定位误差较大且闭环检测精度较低等问题，提出一种融合混合注意力实例分割的视觉同步定位与建图算法。该算法能够动态调整被遮挡物识别权重，在出现遮挡情况时提升对被遮挡物的特征提取与识别能力。同时采用概率去误匹配算法去除错误匹配点对，增加位姿求解及关键帧选取精度，从而更好地修正机器人位姿、提高系统构图的准确率。通过KITTI公开数据集和真实场景对所提算法进行测试，结果表明，所提算法在闭环准确率上与ORB-SLAM2算法相比约提高10.7%，平移误差约减小27.6%，体现了良好的构图能力。

The visual simultaneous localization and mapping algorithm is easy to be interfered under occlusion, which leads to large positioning error and low closed-loop detection accuracy. In this paper, a visual simultaneous localization and mapping algorithm based on mixed attention instance segmentation is proposed, which can dynamically adjust the recognition weight of the occluded object and improve the feature extraction and recognition ability of the occluded object in the case of occlusion. At the same time, a probabilistic mismatching removal algorithm is used to remove the wrong matching point pairs and increase the accuracy of pose solution and key frame selection. In this way, the robot pose can be better corrected and the accuracy of system composition can be improved. The proposed algorithm is tested through KITTI open dataset and real scenes, and the results show that the closed-loop accuracy of the proposed algorithm is about 10.7% higher than ORB-SLAM2 algorithm, and the translation error is about 27.6% lower, reflecting good composition ability.