# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### Hybrid Based Semantic Navigation Systems
The technique resulted in a final map that combined spatial values and semantic names to provide the robot with detailed descriptions of specific locations. The robot then converted the grid map into a cost map, planned a route using [PERSON]'s algorithm, and reached the desired location. The proposed system was tested and evaluated in both virtual and real environments, including hallways and offices. [PERSON] et al. [81] presented a framework for real-time autonomous robot navigation in a dynamically changing environment, with a focus on human-like robot interaction and task planning. Their framework utilized cloud and on-demand databases and consisted of a robot mapping system that emulated the functioning of a human brain. This mapping system incorporated spatial data and performed 3D visual semantic SLAM (simultaneous localization and mapping) to enable autonomous navigation. The robot's memory system was divided into long-term memory (LTM) and short-term memory (STM), which were connected to the autonomous navigation module, learning module, and behavior planner module, collectively forming the robot's behavior and knowledge system. The proposed framework was evaluated using an RGB-D camera, a laser range finder, and a mobile robot simulated in the ROS (robot operating system)-based Gazebo environment. The simulation demonstrated the practical value and effectiveness of the suggested framework for autonomous robot navigation in a realistic indoor setting. In [82], the researchers proposed a safe robot navigation system designed for outdoor and natural environments. The system focused on achieving safe navigation even in the presence of unknown obstacles. They developed a model that classified new sensory inputs into \"known\" and \"unknown\" terrain classes, enabling the robot to navigate safely. The best performance was achieved by combining RGB images, depth, and surface normal data as sensor modalities. This combination allowed robots to operate in environments with unexpected and infrequently occurring barriers, thanks to the researchers' anomaly detection technology. A novel visual navigation scheme for autonomous robots that utilizes only a monocular camera as an external sensor was proposed by [PERSON] et al. [83]. The key concept is to select a target point in an input image based on the results of semantic segmentation, allowing simultaneous road following and obstacle avoidance. The authors also introduced a scheme called virtual LiDAR, which estimates the robot's orientation relative to the current path in a traversable area based on the semantic segmentation results. Experimental results from the Tsukuba Challenge 2019 demonstrated that the proposed scheme enabled a robot to navigate in a real environment with obstacles, such as humans and other robots, given accurate semantic segmentation results. The integration of vision-based with the LiDAR-based methods enhances the robot navigation capabilities by providing both the geometry and semantic information. Therefore, hybrid-based approaches have received considerable attention due to their efficiency in obtaining reliable semantic navigation data. As presented above, several robot semantic navigation systems have been recently proposed with varying levels of efficiency, accuracy, and complexity. The next section discusses the results obtained from existing approaches and defines a set of evaluation metrics that can be employed to assess the efficiency of any semantic navigation system.