## 1 Introduction  
With the rapid development of sensing, computation, manufacturing, and control technologies in recent years, various kinds of robots have been coming into our lives and work gradually, such as unmanned aerial vehicles, robot vacuum cleaners, intelligent vehicles, autonomous disinfection robots, logistics delivery robots, and so on. These different kinds of robots have been transforming our social lives ever-increasingly [1, 2]. For example, during the current COVID-19 pandemic, we always expect to use robots to replace humans to complete the disinfection work in public places, due to different regions in the environment having different risks. Thus, in these environments, one of the most-important prerequisites for the robots to accomplish the task safely, autonomously, and efficiently is that the robot should know its own location relative to the environment. Therefore, it is of great importance to endow the robot with the ability of autonomous navigation. Simultaneous localization and mapping (SLAM) [3] is such a technique that can build the environment map and compute the location in the map simultaneously. With the help of SLAM, when a robot enters an uncertain or even unknown environment, it can make use of the structured environment information to determine its location on the map; it can also reconstruct its surroundings by only relying on its own sensors. Thus, the robot can move and complete specific tasks in a prior unknown and unstructured environment autonomously [4; 5]. SLAM has been widely applied in various applications. Since the robot has to build the environment map and locate its positions by the sensors carried by it, the most-fundamental problem is how to accurately and robustly extract information that can be used for map building and localization. Conventionally, the information extraction techniques have been utilized to obtainfeatures directly from the available sensors by some manual rule-based methods, then these features will be used for map building and localization. With the rapid development of deep-neural-network-based learning methods, we can obtain much high-level semantic information from the environment by the nature of the multi-level information processing mechanism. Thus, the accuracy and robustness of the SLAM system can be improved further by this high-level semantic information [3; 6; 7]. Semantic information plays a positive role in the SLAM system of mobile robots. The semantic information can enhance the robustness against those disturbances in the environment and systems [8; 9; 10]. During the computing of semantic information, the effective information generally flows from low-level, low-accuracy to high-level, high-accuracy by the nature of the hierarchical structure of the deep neural network; thus, this high-level information can adapt to various variations in the environment [11; 12; 13; 14]. Some works showed that the stability of the features can be enhanced by filtering out those features of dynamic or potentially moving objects. Some works showed that this high-level semantic information lowers the sensitivity to sensor noise to some extent, such as straight lines, triangles, and planes, which are more robust than the original sensor data [15; 16]. Some work showed that semantic information can improve the accuracy and reliability of data association by taking advantage of relevance, heterogeneity, and invariance in semantics [17; 18]. On the other hand, semantic information can support the accomplishment of various high-level tasks to a great extent, since these high-level tasks are usually encoded by some natural-language-like semantics, consisting of some specific, abstract symbols, logic, and temporal operators, such as linear temporal logic, signal temporal logic, metric temporal logic, and so on [19; 20; 21; 22].