关键词: 视觉同时定位与建图（SLAM）, 动态场景, 目标检测, 语义分割, 语义地图

Abstract:

Visual SLAM（Simultaneous Localization And Mapping） is the core technology in the field of mobile robots. The traditional visual SLAM cannot be applied to highly dynamic scenes and the map lacks semantic information. This paper proposes a semantic SLAM method for dynamic environment. Firstly, the convolution neural network is used to detect the area of dynamic objects on the image. Secondly, it extracts feature points on the image and removes feature points in the dynamic area and then computes camera pose using static feature points. Finally, the dynamic objects’ map points are removed for building a global semantic map without interference from dynamic objects. The proposed method is tested on TUM datasets, and results show that the proposed method can improve the accuracy of pose estimation by 88.3% in dynamic scenes and construct semantic map without interference from dynamic objects.

Key words: visual Simultaneous Localization And Mapping（SLAM）, dynamic scene, object detection, semantic segmentation, semantic map