An Improved Deep Residual NetworkBased Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network (ResNet) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the antiinterference ability of the algorithm. Then, the robust feature point extraction method is adopted in the frontend module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.

Visual SLAM in Human Populated Environments: Exploring the Tradeoff between Accuracy and Speed of YOLO and Mask RCNN Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics. However, the majority of Visual SLAM algorithms assume a static scenario, limiting their applicability in realworld environments. Dealing with dynamic content in Visual SLAM is still an open problem, with solutions usually relying on direct or featurebased methods. Deep learning techniques can improve the SLAM solution in environments with a priori dynamic objects, providing highlevel information of the scene. This paper presents a new approach to SLAM in human populated environments using deep learningbased techniques. The system is built on ORBSLAM2, a stateoftheart SLAM system. The proposed methodology is evaluated using a benchmark dataset, outperforming other Visual SLAM methods in highly dynamic scenarios.