# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 1 Introduction  
Robots are the next frontier technology that affect many aspects of our society, including industry [1], transportation [2], and services [3]. In recent years, more and more service robots, such as FABO [4], Kejia [5], and NAO [6], have entered the human living environment to improve people's quality of life. In order to complete navigation tasks, the robot needs a map to describe its environment. Traditional map representations include metric, topological, and hybrid ones [7]. The metric map uses grids or geometric features of points, lines, or planes to describe the geometric layout of the environment. To achieve an abstract representation of the environment, a topological map models the environment as a graph, in which vertices correspond to places and edges correspond to the paths. Since the metric map has the characteristic of high localization accuracy and topological map of high path-planning efficiency, the hybrid map takes advantage of both to improve the navigation performance. Based on an occupied grid or hybrid maps, the metric navigation for small or medium-scale environments is relatively mature. However, when it comes to using the robots in the domestic scene, they lack semantic information for users to order them conveniently. For example, the goal point is defined in geometric coordinates, but humans are more inclined to use natural language interaction. Thus, many researchers are committed to building semantic maps, which not only describe the geometric layout but, also, contain the concepts of rooms or objects, to improve human-robot interactions [8]. At present, there is no consensus on the map forms. Some researchers directly add semantic information to the metric map and some combine metric, topological, and concept maps to generate asemantic map with multiple layers [9]. Our ultimate goal is to build a semantic map with multiple layers, which contains a semantic grid layer, a semantic topological layer, and a semantic concept layer, for domestic robot navigation. An object semantic grid map is an occupied grid map with object semantics. One of its obvious advantages is that the robot does not need to query the semantics on the concept map, which can improve the efficiency of human-robot interactions to a certain extent. Additionally, it provides an important guarantee for building the multi-layer map. For example, using the occupied grid map and a room segmentation method, the room topology can be obtained. Based on the object semantics and the relation of rooms and objects, the concept of rooms can be generated. By abstracting the semantic grid and the semantic topological map, the semantic concept map can be formed. It should be noted that there are many types of objects in the home environment. Our map contains only static objects that can determine the concept of the room. For objects that cannot be modeled on the map, we will implement navigation goal reasoning based on the relationship between objects and objects and between objects and rooms on the concept map. Simultaneous localization and mapping (SLAM), which can build an environment map and localize a robot at the same time, is usually used to build metric maps [10]. According to different sensors, it is mainly divided into laser-based SLAM and vision-based SLAM. Laser-based SLAM mainly creates occupied grid maps. Representative algorithms include Hector SLAM [11], GMapping [12], Karto SLAM [13], and Cartographer [14]. Vision-based SLAM mainly builds feature maps, including points [15; 16], lines [17; 18], or planes [19; 20]. Some researchers try to add semantic concepts of objects and rooms based on SLAM to improve the ability of robots' environment understanding. Using machine-learning methods, some directly add room semantic information on the grid map. For example, [PERSON] et al. [21] added the semantic labels of the scene through supervised learning. The works proposed by [PERSON] and [PERSON] [22], [PERSON] et al. [23], and [PERSON] et al.