Localizability of Laser SLAM Robot Based on Deep Learning Positioning is the key technology in the field of mobile robot, and it is also the basis of various tasks such as autonomous movement. Localizability is a measure of the ability to achieve accurate positioning for a given location, and it is an important indicator to further avoid positioning failure in upper tasks. Taking the fusion positioning method based on map matching and trajectory estimation as the research object, this paper analyses its working mechanism, designs different neural networks for different positioning methods, and proposes a neural network model composed of convolutional neural network, recurrent neural network and multilayer perceptron. The model can be used for mobile robot estimate localizability. The simulation and experimental results show that the model can accurately estimate the localizability of the robot in a given map.

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.