# 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 established a Triplet Ontological Semantic Model (TOSM) based on ontology, inspired by recent developments in the employment of ontology in robot applications. They used a CNN model, specifically YOLOv3, as a recognition model for object and place detection. To facilitate navigation through multi-story buildings, they described a multi-layer hierarchical planning scheme that combined reinforcement learning, a traditional planner, and semantic information. The experiments demonstrated the practical usability of their system and showed that TOSM data could effortlessly fulfill navigation tasks in large and dynamic areas. In a similar context of using ontology-based knowledge systems to enhance a basic robot's intelligence, [PERSON] et al. [49] proposed a system that combined visual SLAM with a knowledge base to provide precise and continuous awareness of the surroundings, along with accurate object detection. They developed a semantic mapping technique based on scene geometry and item positions by combining visual SLAM and the RoboEarth ontology. The knowledge base approaches utilized prior knowledge of landmark locations in the semantic map to determine potential object locations and guide the robot's direction when searching for specific objects. In order to include the semantic information of the robot-navigated environment, the authors of [50] proposed using relational models, which included decision rules in the relational information. The provided design facilitates the integration of various object and room categorizations, encompassing both conceptual and physical aspects. This model empowers the robot to comprehend and interact with the environment, enabling it to perform navigation tasks effectively while also making inquiries about the surroundings. It has been demonstrated that employing a database to implement the semantic model is a feasible and time-effective solution for handling the data. Results have shown the capability of achieving goals using user-provided semantic data, enabling a more natural relationship between humans and robots. [PERSON] et al. [51] devised a unique navigation strategy that encompasses corner identification, road-following, and obstacle avoidance. These functionalities were integrated into the proposed method using visual data from a monocular camera. To achieve autonomous mobility, a route is selected based on information from a topological map. The output of semantic segmentation plays a crucial role in accomplishing these tasks. Road-following, a key component of this technology, involves the vehicle navigating while avoiding obstacles within the traversable area identified through semantic segmentation. This approach does not rely on precise environmental mapping or localization; instead, it utilizes repeated coarse localization based on landmarks and follows a road, similar to human behavior, to enhance reliability. Experimental results demonstrated the successful implementation of this strategy in indoor environments, where the robot was capable of traversing hallways, identifying corners, and making turns while effectively avoiding multiple obstacles. A framework for robotic navigation without a global map was proposed by [PERSON] et al. [52]. Their method used a collection of conventional classification modules, including Random Forest (RF) and SVM, to conduct object segmentation and detection given scene photos. They offered a semantic navigation method that could be parsed immediately from spoken language, such as \"enter or leave the room\" or \"follow the corridor until the next door\", etc. Additionally, semantic (or cognitive) maps that include in-depth details about the environment, such as object types, functionalities, and their relationships, were provided. OpenStreetMap (OSM) has limitations, which is somewhat counterintuitive for indoor robotic applications. OSM maintains data about roads, trails, railway stations, and much more all over the world, as well as basic interior architecture, like doors. [PERSON] et al.