This study proposes a semantic map construction method based on object segmentation to solve the problem that maps constructed using the traditional visual simultaneous localization and mapping (SLAM) lack semantic information and cannot understand the scene content to improve the ability of mobile robots to perceive the environment and perform advanced tasks. To begin, the improved semantic segmentation model DeepLab V3+ was used to segment a two-dimensional image to obtain the object's label. Further, the dense map was constructed according to the improved iterative closest point (ICP) point cloud splicing method, and the region growth algorithm was used to segment the three-dimensional point cloud. Finally, the semantic map was constructed by mapping the two-dimensional label to the three-dimensional dense map. Experimental results show that the improved DeepLab V3+ detects objects four times faster than the original method; the improved ICP algorithm is used for point cloud splicing, and the relative trajectory error is reduced by 16.4% when compared to the ORB-SLAM algorithm in the fr/360 sequence of the TUM dataset; finally, compared with the ORB+YOLOv3, ORB+MASK-RCNN, ORB+DeepLab V3+ methods, the proposed method not only reduces the redundant information of semantic map but also builds faster and occupies less storage.