# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 1 Introduction  
In the near future, robots will undoubtedly require a deeper understanding of their operational environment and the world around them in order to explore and interact effectively. Autonomous mobile robots are being developed as solutions for various industries, including transportation, manufacturing, education, and defense [1]. These mobile robots have diverse applications, such as monitoring, material handling, search and rescue missions, and disaster assistance. Autonomous robot navigation is another crucial concept in industrial development aimed at reducing manual effort. Often, autonomous robots need to operate in unknown environments with potential obstacles in order to reach their intended destinations [2, 3]. Mobile robots, also known as autonomous mobile robots, have been increasingly employed to automate logistics and manual operations. The successful implementation of autonomous mobile robots relies on the utilization of various sensing technologies, such as range-finders, vision systems, and inertial navigation modules. These technologies enable the robots to effectively move from one point to another and navigate the desired area [4]. The process by which a robot selects its own position, direction, and path to reach a destination is referred to as robot navigation. Mobile robots utilize a variety of sensors to scan the environment and gather geometric information about the area of interest [5]. Theauthors of [6] revealed that the creation or representation of a map is achieved through the simultaneous localization and mapping (SLAM) approach. Generally, there are two types of SLAM approaches: filter-based and graph-based. The former focuses on the temporal aspect of sensor measurements, while the latter maintains a graph of the robot's entire trajectory along with landmark locations. While grid maps are effective for facilitating the point-to-point navigation of mobile robots in small 2D environments, they fall short when it comes to navigating real domestic scenes. This is because grid maps lack semantic information, which makes it difficult for end users to clearly specify the navigation task at hand [7]. On the other hand, a novel concept called semantic navigation has emerged as a result of recent efforts in the field of mobile robotics to integrate semantic data into navigation tasks. Semantic information represents object classes in a way that allows the robot to understand its surrounding environment at a higher level beyond just geometry. Consequently, with the assistance of semantic information, mobile robots can achieve better results in various tasks, including path planning and human-robot interaction [8]. Robots that utilize semantic navigation exhibit a greater similarity to humans in terms of how they model and comprehend their surroundings and how they represent it. Connecting high-level qualities to the geometric details of the low-level metric map is crucial for semantic navigation. High-level information can be extracted from data collected by various sensors, enabling the identification of locations or objects. By adding semantic meaning to the aspects and relationships within a scene, robots can comprehend high-level instructions associated with human concepts [9]. As illustrated in Figure 1, the design and development of a robot semantic navigation system involve several functions: localization, which entails estimating the robot's position; path planning, which involves determining the available paths in the navigation area; cognitive mapping, which encompasses constructing a map of the area of interest; motion control, which governs how the robot platform moves from one point to another; and object recognition, which involves identifying objects in the area of interest to build a semantic map [10; 11]. The area of robot semantic navigation has received considerable attention recently, driven by the requirement to achieve high localization and path planning accuracy, with the ability to accurately recognize things (objects) in the navigation area.