目的 SLAM（simultaneous localization and mapping）是移动机器人在未知环境进行探索、感知和导航的关键技术。激光SLAM测量精确，便于机器人导航和路径规划，但缺乏语义信息。而视觉SLAM的图像能提供丰富的语义信息，特征区分度更高，但其构建的地图不能直接用于路径规划和导航。为了实现移动机器人构建语义地图并在地图上进行路径规划，本文提出一种语义栅格建图方法。方法 建立可同步获取激光和语义数据的激光-相机系统，将采集的激光分割数据与目标检测算法获得的物体包围盒进行匹配，得到各物体对应的语义激光分割数据。将连续多帧语义激光分割数据同步融入占据栅格地图。对具有不同语义类别的栅格进行聚类，得到标注物体类别和轮廓的语义栅格地图。此外，针对语义栅格地图发布导航任务，利用路径搜索算法进行路径规划，并对其进行改进。结果 在实验室走廊和办公室分别进行了语义栅格建图的实验，并与原始栅格地图进行了比较。在语义栅格地图的基础上进行了路径规划，并采用了语义赋权算法对易移动物体的路径进行对比。结论 多种环境下的实验表明本文方法能获得与真实环境一致性较高、标注环境中物体类别和轮廓的语义栅格地图，且实验硬件结构简单、成本低、性能良好，适用于智能化机器人的导航和路径规划。

Objective Intelligent mobile robots are widely used in industry, logistics, home service, and other fields. From complex industrial robots to simple sweeping robots, the ability of simultaneous localization and mapping is essential. Taking the common low-cost sweeping robot as an example, the scheme adopted is to obtain the distance information between the robot and an object in a plane through 2D lidar, and establish occupation grid map by using laser SLAM(simultaneous localization and mapping) to support robot navigation, path planning, and other functions. With the increasing demand of intelligent service, the geometric map with simple information cannot meet the needs of people. Tasks such as "cleaning the chair" and "going to the refrigerator" require the robot to perceive the environment from the geometric level to the content level. In addition to describing the geometric contour of the environment through grid map, the intelligent robot should have the functions of target recognition, semantic segmentation, or scene classification to obtain semantic information. SLAM is a key technology for a mobile robot to explore, perceive, and navigate in an unknown environment, which can be divided into laser SLAM and visual SLAM. Laser SLAM is accurate and convenient for robot navigation and path planning, but it lacks semantic information. The image of visual SLAM can provide rich semantic information with a higher feature discrimination, but the map constructed by visual SLAM cannot be directly used for path planning and navigation. A semantic grid mapping method based on laser camera system is proposed in this paper to realize the construction of semantic map for mobile robot and path planning. Method To construct a semantic map that can be used for path planning, this paper uses a monocular camera assisted lidar to extract the object level features and the bounding box segmentation matching algorithm to obtain the semantic laser segmentation data as well as participate in the construction of the map. When the robot constructs the occupation grid map, the grid that stores the semantic information is called the semantic grid. The semantic map updates the occupation probability of the grid corresponding to each object and the semantic information in the grid. Then, through the steps of global optimization, semantic grid clustering, and semantic grid annotation, the semantic grid map with object category and contour is obtained. In addition, semantic tasks are published on the semantic grid map, the semantic weighting algorithm is used to identify the easily moving objects in the environment, and the path planning is improved. This system is mainly divided into three parts:semantic laser data extraction, semantic grid mapping and path planning. The input of the system includes the scanning data of 2D lidar and the pictures of monocular camera, and the output is the semantic grid map that can be used for path planning. Semantic laser segmentation data extraction is based on the laser camera system. The laser radar provides the scanning data in a certain height plane in the space. The module projects the laser segmentation in the camera field of view to the image and matches with the detection frame to obtain the semantic laser segmentation data. When the laser segmentation data are acquired, the semantic grid mapping should be carried out simultaneously. When the laser data are used to construct the grid map, the corresponding objects in the grid are marked with semantics and contour according to the semantic information. Moreover, density-based spatial clustering of applications with noise(DBSCAN) clustering algorithm is used to cluster the grids of the same object category and obtain the grid set representing each independent object. Finally, according to the semantic information of objects in the grid set, the corresponding object positions occupying the grid map are marked with different colors and words, and the semantic grid map is obtained. The semantic grid map contains geometric information and content information of environment, which can provide specific semantic objects for robot path planning and assist robot navigation. Adaptive Monte Carlo localization (AMCL) is used to locate a mobile robot in a 2D environment, and the pose information of a robot is determined. The global path planning uses A^* algorithm to plan the global path from the starting point to the target given any target in the map and optimizes it according to the semantic weighting algorithm. Result The hardware of the system consists of a mobile robot and a control machine. The robot platform is composed of a kobuki chassis, an Robo Sense(RS) lidar, and a monocular camera. The laptop of the control computer is configured with 2.5 GHz main frequency, Intel Core i5-7300 HQ processor, GTX 1050ti GPU, and 8 G memory. Semantic mapping and path planning system are deployed as software facilities and run in the robot operating system(ROS) environment. The test system is Ubuntu 16.04. First, the performance of semantic laser data extractions is evaluated, and the detection accuracy of different object detection, laser segmentation, and bounding box segmentation matching is measured. Second, the semantic grid mapping experiment realizes the semantic grid mapping of mobile robot in the corridor and office. Results show that the semantic map in this paper integrates the object-level semantics well on the basis of occupying the grid map, enriches the map content, and improves the readability of the map. Finally, in the path planning experiment, the semantic grid map can perceive the environment content, provide semantic information for robot navigation and path planning, and support the intelligent service of robot. Compared with the route in the original map, the path after semantic discrimination is more flexible and more suitable for the situation of mobile objects in map building. Conclusion Experiments in various environments prove that the method proposed can obtain a semantic grid map with a high consistency with the real environment and labeled target information, and the experimental hardware structure is simple, low cost with good performance, and suitable for the navigation and intelligent robotic path plan.