# NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
## 2 Related Work
LSD-SLAM [34] is the first algorithm to apply the direct method in a semi-dense monocular SLAM, which achieves real-time semi-dense reconstruction based on the relationship between map gradients and the direct method. SVO [35] is fast, but has large errors and no loopback detection. DSO [36] uses a sliding window consisting of keyframes at the back end to obtain accurate camera pose even in weakly textured scenes, running fast and with high robustness. Three-dimensional reconstruction of the indoor environment is the foundation of path planning and navigation for mobile robots. The most direct method is to acquire the point cloud through the RGBD camera on board the robot, and as the number of point clouds saved increases, the robot itself will have insufficient memory, and this method obviously cannot meet the requirements. In recent years, there has been an increasing interest in 3D reconstruction based on RGBD cameras. KinectFusion [37], as the first 3D reconstruction algorithm, relies on depth maps alone to build 3D models without relying on RGB maps, which is based on the principle of using truncated signed distance field (TSDF) to continuously move the camera to obtain objects from different viewpoints for reconstruction, but it relies on GPU parallel computation. Kintinuous [38] adds loopback detection and loopback optimization to this, enabling 3D reconstruction of large scenes, combining iterative closest point algorithm (ICP) and direct method for estimation of camera pose, but inevitably still using GPU implementation. ElasticFusion [39] was the first to represent the map as a surfel model when most 3D reconstruction algorithms use a mesh model to fuse point clouds for reconstruction. ElasticReconstruction [40] processes the input continuous frames in modules and removes the noise of the depth map according to the frames in each module to obtain more accurate reconstruction results. InfiniTAM [41] uses a hash table to store the implicit volume representation, which greatly saves the memory consumption during reconstruction. BundleFusion [42] adopts a parallelized optimization framework, makes full use of the correspondences extracted based on sparse features and dense geometry and photometric matching, and uses bundle adjustment [43] (BA) in the backend to optimize the pose, and its reconstruction results are the best among the current static scenes. Depth information alone cannot enable a robot to locate accurately in an unfamiliar environment, so it is necessary to link the reconstructed map with the semantic information of the objects in the environment, where the semantic reconstruction uses a camera, such as SemanticFusion [44], Qi [45], DynaSLAM [46], MaskFusion [47], Co-Fusion [48], and the method of semantic reconstruction using LiDAR, such as SegMap [49], SuMa [50], SuMa++ [51], and OverlapNet [52]. The above methods rely on GPU processing regardless of whether they use vision or laser for semantic reconstruction, which limits the application in robotics. Of course, there are also some CPU-based methods, such as [53; 54; 55], among which Voxblox++ [53] requires pose estimation using sensors such as RGBD cameras and wheel speed odometers to construct a Voxblox [19] map, and it relies on the GPU. DXSLAM [54], used to extract feature points, and Kimera [55], used to obtain semantic information, are obtained offline. Therefore, these three methods cannot be called semantic SLAM in the strict sense. In summary, the NGLSFusion proposed in this paper has made some improvements in solving these problems, and it has been proven to be more effective through experiments. All the methods used are described in detail in Section 3.