# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### Vision-Based Semantic Navigation Systems
#### 2.2.2 RGB-D Based Systems  
[PERSON] et al. [63] showed how semantic information can be used to infer a path in an outdoor map, especially when there is no clear demarcation between various places in outdoor spaces. The proposed method was based on an automatically constructed object-based representation of the world made from spherical images taken by robots. The experimental study included a labeling layer that depends on an RF classifier and uses RGB-D information for semantic mapping. Moreover, this work presented a novel method for specifying pathways in terms of high-level robot activities. The authors conducted experimental studies using simulated and real-world data to show how their strategy can handle challenging large-scale outdoor environments while addressing labeling problems.  
[PERSON] et al. [64] presented a technique for the semantic dense reconstruction of the operating room scene using multiple RGB-D cameras connected to the da Vinci Xi surgical system, which were registered to it. They developed a unique SLAM (simultaneous localization and mapping) technique to track the robot's attitude in dynamic OR environments and to achieve the dense reconstruction of the stationary objects on the OR table. To validate their proposed system, they collected data from a simulated OR laboratory, using optical tracking and manual labeling as the ground truth. The results indicated that their SLAM system, with its accurate trajectory estimation and semantic scene understanding, significantly improved the robotic perception of the OR environment. This technology has the potential to assist with robot setup, to assist with intraoperative navigation, and to enhance human-robot interaction within surgical procedures.  
[PERSON] et al. [65] presented an innovative approach based on semantic segmentation to extract navigation lines in wheat fields. To accomplish this, they employed an RGB-D camera to construct a dataset that incorporated horizontal parallax, height, and grayscale information (HHG). The technique involves two main steps: firstly, utilizing a fully convolutional network (FCN) for semantic segmentation of the wheat, ground, and background, and secondly, fitting the navigation line within the camera coordinate system by leveraging the segmentation outcomes and the principle of camera pinhole imaging. By training and testing their segmentation model on a dataset of wheat field images, the researchers achieved remarkable accuracy in extracting navigation lines.  
A novel control system for autonomous navigation in vineyards using deep semantic segmentation and edge AI techniques was proposed in [66]. The main objective was to achieve affordable and reliable navigation in vineyard rows without the need for expensive sensors or high computational workloads. The proposed system utilizes a custom-trained segmentation network and a low-range RGB-D camera to leverage semantic information and generate smooth trajectories and stable control. The segmentation maps produced by the control algorithm can also be used for the vegetative assessment of the crop status. The effectiveness and robustness of the methodology were demonstrated through extensive experiments and evaluations in real-world and simulated environments.  
In general, RGB-D-based object detection approaches offer two functions: object classification capability and depth estimation for the detected (recognized) object. RGB-D vision systems offer more reliable semantic information for robot navigation than the RGB vision systems. However, RGB-D vision systems are more expensive than the traditional RGB vision systems and have the same limitations of the existing RGB vision systems (low light and occlusion). In addition, the processing of RGB-D images requires powerful processing capabilities.