# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 4 Indoor Semantic Navigation Lifecycle  
With the advancement of deep neural network algorithms, semantic segmentation algorithms based on deep learning have become widely used. These algorithms contribute to faster and more accurate object recognition. Given the high demand for efficient robot semantic navigation systems across various applications, we list stages that need to be implemented when developing an indoor robot semantic navigation system. Figure 10 illustrates the lifecycle of developing an efficient robot semantic navigation approach.  
The main phases that need to be addressed when developing an efficient robot semantic navigation system are as follows:  
1. Problem domain: The design and development of an efficient robot semantic navigation system depend on the specific problem domain [86]. The choice of perception units, robot design, processing unit, and navigation algorithm varies depending on the robot navigation application. Developing a robot semantic navigation system for domestic applications, for example, is different from developing one for industrial inspection. Therefore, it is significant to identify the problem domain prior to developing a robot semantic navigation system.
2. Navigation subsystem: The navigation system aims to explore possible paths from an initial point to a destination point. SLAM is a widely used navigation approach for constructing maps in unknown environments [87]. However, several research works considered other navigation approaches, for instance, the Unscented Kalman Filter (UKF). Therefore, the need to adopt a reliable navigation system is a crucial task for the purpose of developing an efficient robot semantic navigation system.
3. Vision subsystem: A robust robot semantic navigation system requires the use of an object detection and recognition approach to classify objects in the navigation area and obtain rich semantic map information [88]. There are several object recognition models available with different sets of classes, classification accuracy, processing speed, and memory requirements. However, choosing the most suitable object detection approach for the selected application is a critical task.
4. Robot platform: This includes the robot platform (e.g., Pepper, NAO, Husky) and the development environment (e.g., ROS) used to develop an efficient robot navigation system. Recently, various numbers of robot platforms were made available in the market with several functionalities, capabilities, and prices. On the other hand, several development environments are available that allow developers to implement the robot system for semantic navigation (ROS 2, Matlab R2023b, Webots 2023b,... etc.). Hence, the selection of a suitable mobile robot platform and software environment area is a significant task.  
Figure 10: Main phases for developing an efficient robot semantic navigation system.  
Experimental testbed: This refers to the experimental environment used to validate the efficiency of the developed robot semantic navigation system. In addition, the experiment testbed may include the array of sensors and actuators that are required to perform the task of robot semantic navigation.
6. Validation: Assessing the efficiency of the developed robot semantic navigation system requires the adoption of evaluation metrics. As mentioned earlier, there is currently no academic standard for assessing the efficiency of any robot semantic navigation system. However, this paper presents a set of evaluation metrics that can be used to assess the efficiency of any robot semantic navigation approach.