In the past decades, emerging technologies such as unmanned driving and indoor navigation have developed rapidly, and simultaneous localization and mapping has played unparalleled roles as core technologies. However, dynamic objects in complex environments will affect the positioning accuracy. In order to reduce the influence of dynamic objects, this article proposes an improved simultaneous localization and mapping algorithm combined with semantic segmentation model. First, in the pre-processing stage, in order to reduce the influence of dynamic features, fully convolutional network model is used to find the dynamic object, and then the output image is masked and fused to obtain the final image without dynamic object features. Second, in the feature-processing stage, three parts are improved to reduce the computing complexity, which are extracting, matching, and eliminating mismatching feature points. Experiments show that the absolute trajectory accuracy in high dynamic scene is improved by 48.58% on average. Meanwhile, the average processing time is also reduced by 21.84%.

在过去的几十年中，无人驾驶和室内导航等新兴技术发展迅速，同时定位和地图绘制作为核心技术发挥了无与伦比的作用。但是，复杂环境中的动态对象会影响定位精度。为了减少动态对象的影响，本文提出了一种改进的同时定位与映射算法结合语义分割模型。首先，在预处理阶段，为了减少动态特征的影响，使用全卷积网络模型查找动态物体，然后对输出图像进行掩蔽和融合以获得没有动态物体特征的最终图像。其次，在特征处理阶段，改进了三个部分以降低计算复杂性，提取，匹配和消除不匹配的特征点。实验表明，高动态场景下的绝对轨迹精度平均提高了48.58％。同时，平均处理时间也减少了21.84％。