# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 1 Introduction
[24], use Adaboost, spectral clustering, and random fields of Voronoi, respectively, to generate topological information. [PERSON] and [PERSON] [25] used convolutional neural networks (CNNs) to learn sematic place labels. However, these methods only use grid maps to add room information but cannot generate object semantics. Furthermore, they are not easy to get complex room concepts from. However, if we know the object semantics in a room, we can get the room concepts more easily. For example, [PERSON] et al. [26] employed a Bayesian probabilistic framework to generate room concepts with object detection. As it is easier to use visual sensors to add semantic information, many methods employ visual SLAM to build semantic maps. One of the key parts of these methods is to get semantic information of the environments. For that, object recognition [27; 28], scene recognition [29; 30], the database of the geometric model [31; 32], and human-robot interactions [33; 34], are used to get semantic concepts. With the development of deep-learning technology, the use of object recognition to add semantics has taken the leading position. However, visual semantic SLAM is still in its infancy. Furthermore, visual SLAM has three problems for domestic robot navigation: (1) The generated map is relatively sparse, which is not very useful for the path planning. (2) While visual localization needs a lot of computing resources, domestic robots only have limited ones. (3) Compared with the laser-based method, it is more fragile and not robust enough. Although the robustness of the system can be improved by fusion of the inertial measurement unit (IMU) [35] or robot encoder [36], there are few studies currently using this method to build semantic maps. In this paper, we propose an object semantic grid mapping system with Light Detection and Ranging (LiDAR) and RGB-D sensors. We take advantage of the mature navigation based on LiDAR sensors and the ease of adding semantic information with RGB-D sensors to build object semantic grid maps. First, we use a laser-based SLAM to create a grid map and obtain the robot trajectory. Then, we employ camera interpolation and object detection to generate an object point cloud. To get object-oriented minimum bounding rectangles (OOMBRs), we detect the dominant directions of the room. Based on the affordance space theory, we build object goal spaces for robot goal point selection conveniently and socially. We verify the system on a home dataset, and the experimental results show the effectiveness of our system. In summary, the contributions of this paper are as follows:  
1. An object semantic grid mapping system with 2D LiDAR and RGB-D sensors for domestic robot navigation is proposed. 2. A method for extracting the dominant directions of the room to generate OOMBRs for objects is proposed. 3. Object goal spaces are created for robot goal point selection conveniently and socially. 4. The effectiveness of the system on a home dataset is verified. The remainder of this paper is organized as follows. The system overview is introduced in Section 2. The front-end and back-end of the system are detailed in Sections 3 and 4, respectively. Section 5 presents the experimental results and discussion. Finally, Section 6 concludes this paper.