# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 3 Discussion
In light of this, after conducting various analyses and assessments of the validation metrics adopted in recently developed robot semantic navigation systems, we propose a set of validation metrics that can be used to assess the efficiency of any robot semantic navigation system. These metrics are as follows:  
1. Navigation algorithm: This metric refers to the navigation algorithm employed in the robot semantic navigation system. The SLAM (simultaneous localization and mapping) navigation approach is commonly used and can be divided into laser-based SLAM and vision-based SLAM. Laser-based SLAM establishes occupied grid maps, while vision-based SLAM creates feature maps. However, integrating both categories allows for constructing a rich semantic map. 2. Array of sensors: This metric involves the list of sensors used in the robot semantic navigation system. Typically, a vision-based system and a LiDAR (light detection and ranging) sensor are required to establish a semantic navigation map. The integration of additional sensors may enhance semantic navigation capabilities, but it also introduces additional processing overhead, power consumption, and design complexity. Therefore, it is important to choose suitable perception units. Several robot semantic navigation systems have employed LiDAR- and vision-based sensors to obtain geometry information, visual information, and proximity information, and the fusion of these sensors has achieved better robustness compared to using individual LiDAR or camera subsystems. 3. Vision subsystem: In most semantic navigation systems, the vision subsystem is crucial for achieving high-performance navigation capabilities. Objects can be detected and recognized using an object detection subsystem with a corresponding object detection algorithm. There are currently several object detection classifiers available, each with different accuracy scores, complexity, and memory requirements. Therefore, it is important to choose the most suitable object classification approach based on the specific requirements of the system. 4. Employed dataset: For any object detection subsystem, an object classification dataset is required. However, available vision datasets differ in terms of the number of trained objects, size, and complexity. It is important to select a suitable vision dataset that aligns with the robot semantic application [84]. 5. Experiment testbed: This metric refers to the type of experiment conducted to assess the efficiency of the developed robot semantic navigation system. The system may be evaluated through simulation experiments or real-world experiments [85]. In general, semantic navigation systems require real-world experiments to realistically assess their efficiency. 6. Robot semantic application: This metric refers to the type of application for which the developed system has been designed. It is important to determine the specific application in which the navigation system will be employed, since the vision-based system needs to be trained on a dataset that corresponds to the objects that may exist in the navigation environment. Additionally, the selection of suitable perception units largely depends on the structure of the navigation environment. 7. Obtained results: This metric primarily concerns the results obtained from the developed robot semantic navigation system. As observed in the previous section, researchers have employed different sets of evaluation metrics to assess the efficiency of their systems. Therefore, it is necessary to adopt the right set of validation metrics to assess the efficiency of a developed robot semantic navigation system.