# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### Vision-Based Semantic Navigation Systems
#### 2.2.1 RGB Based Systems  
In [30], the authors developed a semantic segmentation network for an indoor robot navigation system. The system utilized a Residual Neural Network (RNN) and adoptedResNet-18 transfer learning to discriminate between the floor, walls, and obstacles such as furniture and boxes. Real experiments, using a robot platform, laptop, and webcam, were conducted to assess the classification accuracy of the developed system. The work presented in [31] aimed to develop a visual navigation scheme based on a topological map, similar to the system used by humans. Additionally, a road-following scheme using a combination of image processing methods resulted in efficient semantic segmentation. The proposed system identifies a target point towards which the robot moves. Experimental results on a 500 m course on a university campus demonstrated that the robot controlled by the proposed approach could navigate the course adequately. In [32], the authors created a vision-based framework for an indoor robot navigation system. The system leverages semantic segmentation and deep-learning techniques to achieve the precise and efficient mapping of indoor scenes. They introduced a path extraction scheme that utilizes deep convolutional neural networks (DCNNs) and transfer learning for semantic pixel-wise segmentation. The authors of [33] suggested using high-level semantic and contextual features included in the segmentation and detection masks and learning the navigation policy from observations. Furthermore, an optimal path-planning algorithm was employed as stronger supervision to estimate progress towards the goal point. The work presented in [34] focused on semantic knowledge and how it can be effectively employed in robot task planning. The authors explored different ways in which semantic knowledge can be used for task planning in robot navigation. They also defined a well-founded semantic map that combines spatial knowledge. In [35], the authors proposed a location and autonomous navigation system using entropic vision and the visual bug algorithm. The proposed navigation system was intended for emergency situations to help people find an exit door along a secure route. The algorithm employed a visual topological map to autonomously navigate the environment of interest. The implemented bug algorithm does not require knowledge about the robot's coordinates, as the robot uses its own location method during navigation. Experimental results showed that the hybrid algorithm is highly robust when the robot navigates in unknown environments. The work presented in [36] involved developing a method that effectively deals with simultaneous localization and mapping (SLAM) problems for scenarios with dynamic objects. The method utilizes semantic descriptors extracted from images with the assistance of a knowledge graph. The proposed system leverages the knowledge graph to build a priori movement relationship between entities and constructs high-level semantic information. The authors of [37] developed a vision system to detect objects in natural environments using a real robot platform. They employed an SVM classification model to classify objects in the area of interest. The main concept is to capture real-time images in the navigation environment, process and extract features of the objects, and then employ an SVM classifier to recognize the objects. The work presented in [38] involves a vision-based navigation control system for a wheeled robot traveling in outdoor environments. The proposed system includes the use of a deep CNN for pixel-wise segmentation to identify road regions. A fuzzy controller was designed to command the robot's activities. Several experiments were conducted to verify the essential capabilities of the proposed navigation system using the deep architecture of CNN. The authors of [39] discussed the process of establishing semantic maps, which are rich maps containing high-level features, and explained how to label entities in such maps.