# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### Vision-Based Semantic Navigation Systems
#### 2.2.1 RGB Based Systems
They also explored the use of semantic maps to enable context-aware navigation behaviors. Additionally, they presented a method for extracting metric goals from semantic map landmarks and planning human-aware paths that consider the personal spaces of people. They demonstrated context-aware navigation behaviors through a multi-modal interaction model for annotating semantic landmarks. In [40], a three-layer perception approach based on transfer learning was proposed. The approach included a place recognition model used to recognize different regions in rooms and corridors, as well as a side recognition model to determine the robot's rotation. Semantic navigation was performed using a single camera, and real indoor environment experiments were conducted to showcase the robustness and effectiveness of the perception approach. The authors of [41] introduced a reinforcement learning-based local navigation approach that learns navigation behavior based on visual observations to handle dynamic environments. They developed an efficient simulator called ARENA2D, which generates highly randomized training environments and provides semantic information to train robot systems. The developed system was validated in terms of safety and robustness in highly dynamic environments. The work presented In [42] focused on offering mobile robots the ability to detect objects for semantic navigation. Two different object detection methods were proposed: contour detection and a descriptor. By combining both methods, the system achieved efficient classification accuracy even in dynamic environments. The aim of the work presented in [43] was to build a semantic map that investigated the semantic meanings of navigation elements, particularly place names. The developed system aimed to define place names inside a building where the robot navigated, to present corresponding facts and rules using predicate logic expressions, and to establish a conceptual system that is semantically relevant to humans. The proposed semantic map method utilized predicate logic to represent the building's structure and link it with place names. The system presented in [44] focused on semantic navigation without employing visual object detection. Instead, the system adopted path planning based on movable area extraction from input images using semantic navigation. The system utilized a novel dataset for semantic segmentation, and the results showed that it achieved more than 99% accuracy in extracting the movable area using ICNet. The authors of [45] introduced a semantic region map model for complex environments. The developed system demonstrated how navigation points (semantic positions) could be deduced from the map using a semantic description of the environment. These semantic positions were then connected to form a graph, where the edges were labeled with robot actions based on the semantic description of the robot's capabilities. The work presented in [46] focused on semantic mapping and semantic-boosted navigation. The goal was to develop an intelligent robot system capable of real-time belief-based scene understanding. The system employed object detection and recognition methods, along with a path creation strategy based on semantic information. Scene understanding was achieved using a CNN model with multiple functions, and the results were transformed into robotic beliefs using Bayes filtering to ensure temporal coherence. In [47], the authors discussed map building and the localization of mobile robot exploration tasks in planetary areas using semantic navigation maps. The semantic navigation map was generated from descent and landing imagery in a robotic exploration mission. The semantic navigation system was implemented using a virtual robotics testbed and utilized different localization algorithms. [PERSON] et al. [48] utilized ontology to integrate semantic information into robotic systems, providing the robot with mapping, recognizing, and planning abilities similar to those of a human.