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Intelligent optical sensing technologies play important roles in many fields, one of which is to help unmanned devices such as UAVs, autonomous mobile robots and intelligent robots to achieve accurate localization and mapping. With the advancement of Industry 4.0 and intelligent manufacturing, the use of autonomous mobile robots has become an important indicator of a country's industrial modernization. As the core issue in the research of autonomous mobile robots technology, autonomous localization and mapping technology has been the focus and difficulty of many scholars at present. Through the efforts of early researchers and engineers, the localization and mapping technology of autonomous mobile robots in simple static environment has achieved fruitful results, and is also playing an important role in the practical industrial application of autonomous mobile robots. However, when the autonomous mobile robots are faced with more complex or changing surrounding environment, the traditional localization and mapping methods based on geometric features such as points and lines can not achieve more accurate results, and even produce many wrong data to hinder the normal operation of the autonomous mobile robots. In this paper, combined with the characteristics of the complex dynamic environment that autonomous mobile robots will encounter in actual work, we propose a method to obtain and utilize the relatively advanced semantic information in the surrounding environment and use it for autonomous mobile robot localization and mapping. The method of this paper uses deep learning technology to mine more advanced semantic information based on the traditional method obtaining the geometric information of the environment, so that the autonomous mobile robot can generate advanced recognition and cognition of the objects inthesurrounding environment, thus assisting it to complete more accurate localization and mapping.
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