# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### Hybrid Based Semantic Navigation Systems  
Hybrid-based approaches rely on the utilization of two main perception systems: LiDAR and vision-based (RGB cameras). The former is capable of building a geometry map, while the latter can recognize objects in the area of interest. The adoption of these two perception systems offers a semantic map with rich navigation information. In [67], a new visual navigation method for mobile robots was introduced. The proposed method utilizes a combination of a sketched map and a semantic map for robot navigation. Unified tags were employed to aid in recognizing landmarks. By using a hand-drawn map, the robot could easily identify referenced objects against complex backgrounds and estimate the approximate distances between them based on the recently updated scale. This method proved effective for unknown and dynamic environments, offering robust recognition algorithms and eliminating the need for capturing photos in advance. The research described in [68] introduced a semantic SLAM framework designed for rescue robots. The system is capable of generating geometric maps along with semantic labels derived from CNN semantic segmentation. To train the CNN model, an RGB-D dataset from the RoboCup Rescue-Robot-League (RRL) competition environment was utilized. By leveraging semantic information, the rescue robot can identify viable paths in intricate environments. The results obtained from the study demonstrated the effectiveness of the proposed semantic SLAM framework in creating a dense semantic map within complex RRL competition environments, thereby reducing the navigation time to reach desired destinations. The work presented in [69] involved a language-driven navigation system for commanding mobile robots in unknown outdoor environments. The proposed system aims to facilitate natural interaction in human-robot teams. The approach addresses the problem of spatial navigation and relations using inverse optimal control to learn navigation modes and a Bayesian model to balance perception uncertainties with spatial constraints. Semantic grid maps were suggested by [PERSON] et al. [70] as a supplement to the occupancy grid map, incorporating the semantics of items and rooms. This enables robots to navigate safely using user-friendly modes. The authors employed fully convolutional region-based networks for object classification, including ten classes of objects. However, the proposed object detection subsystem was unable to differentiate between distinct manifestations of the same object. To address this issue, the authors employed various techniques, such as density-based grouping, item size, and grid map information. Consequently, the authors proposed using a high number of clusters and then utilizing the topological spaces of objects and domestic common sense to merge the cluster results. The experiments were conducted using the FABO robot, which is equipped with 13 sonar sensors and stereo-vision, in a homelike environment comprising three rooms and domestic objects. The findings of the experiment demonstrated that the mapping technique accurately represented the surroundings and enabled the robot to perform home navigation duties in a user-friendly manner. In a similar context, [PERSON] et al. [71] developed an object semantic grid-mapping system using 2D LiDAR and RGB-D sensors to incorporate semantic information into the occupied grid maps, allowing robots to select social goals and behave in a human-friendly manner. For the object detection task, the authors proposed using the well-known deep-learning algorithm called Mask R-CNN to obtain semantic labels, probability values, and bounding rectangles of six types of objects in the captured images. The Robot@Home dataset was used to test the proposed system, and the results demonstrated its satisfactory performance. The experimental results showed that the developed system achieved an average precision of 68.42% and an average recall of 65%.