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

Simultaneous localization and mapping (SLAM) plays an important role in many fields, one of which is to help unmanned devices such as drones, self-driving cars and intelligent robots to achieve precise positioning and mapping. However, when facing complex or changing surroundings, especially when healthcare robots face a large number of mobile healthcare workers and patients in wards, the hospital environment is relatively complex, and the traditional positioning and mapping methods based on geometric features, such as points and lines, are not able to achieve accurate positioning and mapping results for healthcare robots. This paper mainly focuses on the characteristics of complex dynamic environment, and proposes a method to obtain semantic information of surrounding ring and dynamic point culling strategy for robot localisation and mapping. Experiments show that compared with the current popular SLAM technology, the semantic-based SLAM technology proposed in this paper can help the robot to obtain more accurate localisation and mapping, in addition, using this semantic information, the robot can also better identify the surrounding objects, which lays the foundation for performing more complex tasks.