Semantic SLAM system for mobile robots based on large visual model in complex environments

Intelligent optical detecting tracking technologies play important roles in many fields, one of which is to help unmanned devices such as UAVs, autonomous vehicle and intelligent robots to achieve accurate localization and mapping. For medical and nursing robots, the first step in participating in the treatment and nursing process is to accurately locate their location in the ward, and perceive the surrounding environment of the ward.However, when faced with more complex or constantly changing surrounding environments, especially when medical and nursing robots facing a large flow of medical personnel and patients in wards, the hospital environment is relatively complex, then traditional positioning and mapping methods based on geometric features such as points and lines cannot achieve accurate results for medical nursing robots. In this paper, combined with the characteristics of complex dynamic environments encountered in actual wards, we propose a method to obtain high-level semantic information in the surrounding environment and use it for medical and nursing robot’s localization and mapping. Experiments have shown that the semantic based SLAM technology proposed in this article can help medical and nursing robots achieve more accurate localization and mapping results compared to the current popular SLAM technologies, and the use of semantic information can also enable medical and nursing robots to recognize medical devices, laying the foundation for performing other higher level tasks.