# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 3 Discussion
Others employ models such as YOLO, MobileNet, ResNet, and Inception. The choice of an  
Figure 4: The distribution of robot semantic navigation systems based on the employed technology. object recognition model primarily depends on the processor and memory capabilities of the system. Figure 6 provides an overview of the employed object recognition approaches in recently developed semantic navigation systems, offering statistics on their usage. The adoption of object recognition models is fundamentally reliant on the availability of an object recognition dataset. Different datasets vary in terms of the number and type of objects classified within them. Therefore, it is crucial to select the most suitable dataset for a given robot semantic navigation scenario. Figure 7 illustrates the overall distribution of recently developed robot semantic navigation systems based on the employed object recognition dataset, providing an overview of the usage across different datasets. The choice of sensing technology, or perception units, plays a significant role in obtaining an efficient semantic map. Various sensing technologies have been used to  
Figure 5: Overall distribution of the existing research works based on employed processor technology. Figure 6: Overall statistics on employed object recognition approach. Figure 7: Overall statistics on employed object recognition dataset. gather data about the surrounding environment. Most researchers have focused on using RGB cameras to build semantic maps. RGB cameras are effective for object detection and classification, but they cannot accurately estimate the distance to objects in the area of interest and do not provide geometry information. In contrast, RGB-D cameras offer better results as they can recognize objects and measure the distance to objects in the area of interest, leading to the construction of a more efficient semantic map. Figure 8 provides an overview of the employed sensing technologies in recently developed systems, presenting statistics on their usage. On the other hand, semantic navigation systems that combine RGB cameras and LiDAR units have the potential to construct a semantic map with rich information. However, these technologies have received less attention due to the higher requirements in terms of processing capabilities, complexity, power consumption, and cost. For validation purposes, researchers have the option to employ various experimental testbed environments. Initially, researchers often build their concepts using theoretical models to validate the efficiency of their systems. However, for more reliable and confident results, researchers typically perform either simulation experiments or real-world experiments to validate the effectiveness of the proposed robot semantic navigation system. Figure 9 illustrates the distribution of existing robot semantic navigation systems based on the type of experiment testbed employed. From our perspective, validating robot semantic navigation systems solely through simulation experiments can be challenging. This is because robots need to accurately recognize objects in the area of interest to build an efficient semantic map, and real-time geometry information may be difficult to replicate accurately in simulation environments. Consequently, a large number of researchers rely on real-world experiments for  
Figure 8: Overall statistics on employed sensing technology. Figure 9: Statistics on the employed development environments. validation purposes, as they provide a more realistic and practical assessment of the system's performance. Recent research studies [71; 75] have highlighted that the academic community has yet to establish a unified standard for the validation of semantic maps. As a result, there is currently no clear set of validation parameters for assessing the efficiency of robot semantic navigation systems.