# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 3 Discussion  
Understanding the environment is a crucial aspect of achieving high-level navigation. Semantic navigation, therefore, involves incorporating high-level concepts, such as objects, things, or places, into a navigation framework. Additionally, the relationships between these concepts are utilized, especially with respect to specific objects. By leveraging the knowledge derived from these concepts and their relationships, mobile robots can make inferences about the navigation environment, enabling better planning, localization, and decision-making. As discussed in the previous section, existing robot semantic navigation systems can be categorized into three distinct types. Figure 4 illustrates the distribution of these systems across the categories. It should be noted that while LiDAR-based systems offer simplicity in terms of data processing tasks, they have limitations when it comes to object classification. Compared to vision-based systems, LiDAR systems have a more limited capacity for recognizing a wide range of labels. As a result, the development of technologies for robot semantic navigation systems that rely solely on LiDAR is less necessary. On the one hand, vision-based approaches have gained significant attention in the field of robot navigation, particularly for building semantic navigation maps. However, these systems lack the geometry information that can be obtained from LiDAR sensor units. In contrast, hybrid-based approaches offer the best combination of geometry and semantic information, allowing for the creation of maps with rich details that include both geometry and object classification and localization. Robot perception plays a crucial role in the functioning of autonomous robots, especially for navigation. To achieve efficient navigation capabilities, the robot system must possess accurate, reliable, and robust perception skills. Vision functions are employed to develop reliable semantic navigation systems, with the primary goal of processing and analyzing semantic information in a scene to provide scene understanding. Scene understanding goes beyond object detection and recognition, involving further analysis and interpretation of the data obtained from sensors. This concept of scene understanding has found practical applications in various domains, including self-driving cars, transportation, and robot navigation. Figure 4 demonstrates that vision-based semantic navigation is the most commonly employed technology in recently developed robot semantic navigation systems for indoor environments. Semantic navigation systems require intensive processing capabilities. Therefore, it is crucial to employ an efficient processor unit to handle and process the data received from onboard sensors, including LiDAR and RGB camera units. Processing RGB and RGB-D images necessitates high processing capabilities for tasks such as object detection, recognition, and localization. Many researchers utilize personal computers integrated with the robot platform to perform these processing tasks. However, incorporating a computer (such as a laptop) onto the robot platform adds extra size, power consumption, cost, and complexity. Alternatively, some researchers have employed processors like Raspberry Pi, Jetson Nano, or Intel Galileo to handle the processing tasks. Figure 5 illustrates the distribution of existing robot semantic navigation systems based on the employed processor technology. Raspberry Pi computers are cost-efficient, adaptable, and compact, but they may struggle with complex processing tasks. Jetson Nano offers better specifications than Raspberry Pi but adds an additional cost to the overall robotic system. Robot semantic navigation systems necessitate the use of object recognition models to classify various types of objects within the area of interest. Several object recognition models have been developed for this purpose. For example, many researchers choose to develop customized convolutional neural network (CNN) models to classify objects of interest.