语义信息对于移动机器人理解环境内容、执行复杂任务至关重要，针对ORB-SLAM2构建的点云过于稀疏、缺乏语义信息、点云所占存储空间大等问题，提出将目标检测算法与视觉SLAM（同时定位与地图构建）技术紧密结合，构建环境的稠密点云语义地图。首先，通过目标检测网络YOLO v3及对象正则化准确获取物体的2D标签，并经过ORB-SLAM2算法构建环境的稀疏点云地图，通过含有2D标签的彩色图像和对应的深度图像以及关键帧来生成含有语义信息的稠密点云标签，使用基于图的分割算法对稠密点云进行分割，再将点云标签与分割后的点云进行融合，进而构建环境的稠密点云语义地图。文中方法在TUM公开数据集上进行试验，实验表明可以构建出效果较好的语义地图。与传统的ORB-SLAM2相比，此系统在构建地图的过程中，相机的绝对位姿误差和绝对轨迹误差分别减少了16.02%和15.86%，提高了建图精度。为了减小点云地图的存储空间，方便移动机器人进行避障和导航，最终将所构建的语义地图转换为八叉树地图。

Semantic information is essential for mobile robots to understand the content of the environment and perform complex tasks. Aiming at the problem that the point clouds constructed by ORB-SLAM2 is too sparse and lacks semantic information, a dense point cloud semantic map of the environment by combining the object detection algorithm with visual SLAM technology was constructed. First of all, the object detection network YOLO v3 and object regularization were used to accurately obtain the 2D label of the object. At the same time, the ORB-SLAM2 algorithm was used to construct the environment's sparse point cloud map. The color image with 2D labels, corresponding depth images, and key frames were used to generate dense point cloud labels with semantic information. Then the graph-based segmentation algorithm was used to segment the dense point cloud, and the point cloud labels were fused with the segmented point cloud so as to construct a dense point cloud semantic map of the environment. The proposed method was tested on the TUM public data set and the experimental results show that the method can construct a better semantic map. Compared with the traditional ORB-SLAM2 algorithm, this system reduces the absolute pose error and absolute trajectory error of the camera by 16.02% and 15.86% respectively, in the process of constructing the map, which improves the mapping accuracy. In order to reduce the storage space of point cloud maps and facilitate mobile robots' navigation and avoidance, the constructed semantic maps are finally converted into octree maps.