# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 1 Introduction
Recently, there have been several survey articles that have focused on various aspects of this field. For example, the work presented in [12] surveyed the use of reinforcement learning for autonomous driving, whereas the work discussed in [13] presented an overview of semantic mapping in mobile robotics, focusing on collaborative scenarios, where authors highlighted the  
Figure 1: Main components of robot semantic navigation. importance of semantic maps in enabling robots to reason and make decisions based on the context of their environment. Additionally, the authors of [14] addressed the challenges involved in robot navigation in crowded public spaces, discussing both engineering and human factors that impede the seamless deployment of autonomous robots in such environments. Furthermore, the work presented in [9] explored the role of semantic information in improving robot navigation capabilities. The authors of [15] presented a comprehensive overview of semantic mapping in mobile robotics and highlighted its importance in facilitating communication and interaction between humans and robots. Several methods and advancements in semantic mapping for mobile robots in indoor environments were discussed in [16], where the authors emphasized the importance of attaching semantic information to geometric maps to enable robots to interact with humans, perform complex tasks, and understand oral commands. On the other hand, the work discussed in [17] aimed to offer a comprehensive overview of semantic visual SLAM (VSLAM) and its potential to improve robot perception and adaptation in complex environments. In addition, the authors of [18] presented an overview of the importance of semantic understanding for robots to effectively navigate and interact with their environment. The authors emphasized that semantics enable robots to comprehend the meaning and context of the world, enhancing their capabilities. Moreover, the work presented in [19] highlighted the recent advancements in applying semantic information to SLAM, focusing on the combination of semantic information and traditional visual SLAM for system localization and map construction. As mentioned earlier, numerous research studies have surveyed the works that target the area of robot semantic navigation for indoor environments. Nevertheless, this paper distinguishes itself from existing works by specifically concentrating on semantic navigation systems designed for indoor settings. This paper goes on to categorize and discuss the various navigation technologies utilized, such as LiDAR, Vision, or Hybrid approaches. Additionally, it delves into the specific requirements for object recognition methods, considering factors such as the number of categorized objects, processing overhead, and memory requirements. Lastly, this paper introduces a set of evaluation metrics aimed at assessing the effectiveness and efficiency of different approaches to robot semantic navigation. The remaining sections of this paper are organized as follows: Section 2 discusses the existing indoor robot semantic navigation systems, while Section 3 examines and compares the results obtained from these systems. Additionally, a list of evaluation metrics is presented. In Section 4, we discuss the lifecycle of developing an efficient robot semantic navigation system. Finally, Section 5 concludes the work presented in this paper.