# NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
## 2 Related Work  
In recent years, the fusion of geometric maps of indoor environments with semantic information to build semantic maps to link objects with semantic concepts has become a popular research area in the field of mobile robotics, but there is no SLAM system that runs in real time without using GPU and concurs with both robot pose and semantic information. Indoor semantic SLAM generally consists of two modules: a VO (or VIO) module based on visual SLAM, and a semantic point cloud fusion module. When the mobile robot is in an unknown environment, VO then locates the current location of the robot and draws a sparse map of the environment. The basic idea is to extract and match the feature points of different frames of the same scene through a continuous frame with translation and rotation, calculate the transformation between frames, and select and save keyframes for relocalization, loop closure, and map building. There are two main methods for VO (or VIO) to extract feature points from images: feature point method and direct method. The application of the feature point method in VO (VIO) can be traced back to some SLAMs based on monocular vision [21; 22; 23; 24; 25]. Among them, MonoSLAM [21] and RT-SLAM [22] use extended Kalman filter (EKF) to implement SLAM. [PERSON] [23] proposed an SLAM method based on particle filter to improve the effect of system nonlinearity to EKF by introducing a traceless Kalman filter, but it increased the computational complexity. [PERSON] [24] chose to use nonlinear optimization as the back-end-dominant, and introduced keyframe mechanism and multithreading mechanism to improve the efficiency of the algorithm when the mainstream visual SLAM used EKF filter to process the back-end data at that time, but it was susceptible to motion blur and camera rotation and only suitable for small scenes due to its 2D-2D image matching algorithm, and did not consider the global loop. ORB-SLAM [25] extended on this basis by using ORB features to establish short-term and medium-term data associations, establishing co-views to increase visual relationships and map construction, and using the bag-of-words database DBoW2 [26] for loop closure and relocation to achieve long-term data association. Since monoculars cannot obtain accurate scale information, and the constructed maps are inaccurate, ORB-SLAM2 [13] adds stereo camera and an RGBD camera to obtain an absolute scale. ORB-SLAM3 [14] fuses the camera and the inertial measurement unit (IMU) on this basis for joint optimization of the camera pose. In terms of feature point extraction, eSLAM [27] accelerates ORB feature point extraction on an FPGA [28] platform. GCNv2 [29] is improved on the basis of the deep learning network GCN [30] to speed up feature point matching. SuperPoint [31] proposed a fully convolutional neural network architecture that uses the self-supervised domain of homographic adaptation to train images to detect key points and descriptors. Although the theory of feature point method is mature and widely used, it is difficult to match feature points for no texture or weak texture, and the extraction of feature points consumes a lot of resources. Therefore, an optical flow method was proposed, which uses optical flow to track key points instead of calculating descriptors, and it is obviously superior to the feature point method in time consumption. When processing frames, it needs to ensure that the ambient luminance is constant and that there is continuous \"small motion\" between frames, which can be subdivided into dense optical flow (such as Horn-Schunck optical flow method [32]) and sparse optical flow (such as Lucas-Kanade optical flow method [33]), depending on its working principle. Based on the optical flow method, the direct method mentioned above was thus created, and both estimate the camera motion by minimizing the photometric error, except that the direct method further optimizes the camera pose using the least squares method.