# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
###### Abstract
Among them, NetVLAD [16] is an excellent network in current scene recognition, which fuses image features learned from convolutional neural networks with Vector of Locally Aggregated Descriptors (VLADs) to construct a neural network. The NetVLAD network can convert the input image into a feature vector, which is highly distinguishable and expressive and can recognize different viewpoints, lighting, and deformations in the same scene, thus improving the accuracy of loop closing. Semantic SLAM is one of the research directions in which visual SLAM has more advanced information, which can not only establish the spatial structure of the environment but also obtain the semantic information such as position, label, etc. of any object in the environment. It can realize the construction of richer map information. [PERSON] et al. [17] combined ORB-SLAM2 and SSD [18] into a new coupling framework, Detect-SLAM, and proposed real-time propagation of motion probabilities of key points to solve the problem of delayed target detection threads. Semantic information is used to eliminate the negative effects caused by moving objects in SLAM. The framework aims to improve the efficiency of target detection and sensitivity to the viewpoint transformation problem, but the real-time performance of the system needs to be further optimized. [PERSON] et al. [19] proposed a DS-SLAM scheme that improves on the ORB-SLAM2 system by combining a visual SLAM algorithm with a semantic segmentation network, utilizing SegNet [20] to obtain semantic information in a dynamic scene, and filtering the dynamic part using motion consistency detection based on the optical flow pyramid algorithm. This method improves the accuracy of the position estimation, but the limited types of objects that can be recognized by the semantic segmentation network in this scheme restrict its application. [PERSON] et al. [21] proposed the DynaSLAM scheme, also based on the ORB-SLAM2 framework, which uses the Mask R-CNN [22] network combined with a multi-view geometric model to realize the detection and rejection of dynamic targets. The test results demonstrate that DynaSLAM has good stability and high localization accuracy under monocular, binocular, and RGB-D camera operating conditions. However, the system is difficult to run in real time. [PERSON] et al. [23] used Mask R-CNN as an instance segmentation network and PWC-Net [24] dense optical flow network for extracting and tracking moving targets in images, which is computationally intensive and very dependent on hardware device conditions. By comparing and analyzing the above visual SLAM algorithms combined with deep learning, deep learning in visual SLAM is usually applied in the following modules: feature extraction, feature matching, loop closing, mapping, or adding more advanced semantic information. The neural network is used to learn more robust feature representations, to improve the validity and accuracy of the feature point extraction, to improve the ability to correlate the images and the accuracy of the feature matching, and to help construct a higher-quality map and give richer information to improve the performance of the whole system. To further improve the localization accuracy of visual SLAM algorithms for AGVs in dynamic environments and the problem of constructing semantic maps so as to provide better assistance for autonomous navigation and path planning of mobile robots, and to balance localization accuracy and real-time performance, a semantic visual SLAM algorithm is proposed that integrates the semantic segmentation network DeepLabV3+, LK optical flow, and background restoration. This study's primary contributions can be summarized as follows:  
1. To reduce the interference of dynamic targets on system localization and mapping, the ORB-SLAM2 system is used as the basic framework and improved on this basis. The semantic segmentation network and optical flow method are combined to achieve the detection and rejection of dynamic targets.