# NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
## 1 Introduction  
As intelligent mobile robotics teams continue to evolve and robot-oriented technologies become more diverse, measuring their ability to perceive the geometry of objects inside a scene, attach accurate semantic labels, and reconstruct the recognized objects with semantic information in a map is a test of the robot's ability to properly understand its environment. When a robot comes to work in an unfamiliar environment, the ability to build an accurate map helps it identify where obstacles are and locate itself in the environment, and the semantic information it obtains provides the robot with the ability to understand its surroundings and carry out commands given by humans, such as \"bring the food there\" and \"clean the trash to the left of the refrigerator\". There are many excellent 3D reconstruction algorithms available today, such as Multi-View Stereo [1], Structure from Motion [2], SLAM [3], etc. Meanwhile, there are also breakthroughs in semantic segmentation algorithms based on deep learning, such as [4; 5; 6; 7], but there is not much intersection between these two fields. In recent years, more attention has been focused on combining 3D reconstruction algorithms with deep learning algorithms and building semantic SLAM systems, and so far, these two research fields have been revitalized by combining witheach other. The more popular ones at this stage are dynamic semantic SLAM [8, 9] and 3D semantic SLAM [10, 11, 12]. However, the combination of the two fields also brings some new problems. The 3D reconstruction algorithms rely on CPU, various algorithms based on deep learning rely on GPU, and the combination of the two will make the whole system work at high intensity, while mobile robots are mostly used as commercial service-oriented robots, considering their commercial cost performance, so high performance processors will not be configured on the robots. For this problem, this paper proposes a lightweight indoor semantic map construction algorithm non-use GPU named NGLSFusion. In the past few years, visual SLAM has been widely used, mainly because its framework is very stable and mature, and the visual-based images store rich texture information, in which some excellent algorithms have also achieved good results, such as ORB-SLAM2 [13], ORB-SLAM3 [14], VINS-Mono [15], etc. Compared to most current visual odometry (VO) and visual-inertial odometry (VIO) or 3D semantic reconstruction algorithms, NGLSFusion combines both, and the algorithm is capable of accurate localization and real-time map building, relying only on the CPU on mobile robots with low equipment performance. The VO part of the algorithm uses an excellent open-source visual SLAM, ORB-SLAM2 [13]. In this paper, an RGBD camera is used because it can provide accurate depth and scale information to improve the robustness of the system. In the algorithm, the non-keyframes coming in from the front end are combined using ORB features [16], optical flow method, and direct method to jointly obtain the keyframes and extract the feature points on the keyframes, and then PnP-Ransac is used to optimize the camera pose for the keyframes and feature points. LinkNet [17], a lightweight network using encoding and decoding connections, is used to identify indoor environmental objects to obtain object semantic segmentation results, which fits well with our mobile robot platform due to its real-time segmentation and fast operation. Since the algorithm in this paper is designed to run in real time, without GPU support, the segmentation effect will be much reduced, so we use Intel's neural network gas pedal OpenVINO to optimize the pretrained model and speed up the model inference, so as to achieve real-time recurrent inference without relying on GPU. Indoor semantic map construction is performed using OctoMap [18] (for robot navigation) and Voxblox [19] (for building more detailed textured maps).