# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### Hybrid Based Semantic Navigation Systems
The effectiveness of these solutions was validated through experiments conducted in real-life scenarios. In their work in [77], [PERSON] et al. proposed a strategy based on semantic segmentation for detecting and identifying various object classes within an indoor environment. They employed a deep neural network model that integrated object detection and semantic classification in a visual-based perception framework. The system generated a 2D map of the environment, providing information about the semantic object classes and their respective locations. To gather the necessary data, multiple sensors were utilized, including odometers, an RGB-D camera, and LiDAR. The authors also employed a 3D model-based segmentation technique and a CNN-based object detection method called YOLO to accurately locate and classify different object classes within the scene. In their research in [78], [PERSON] et al. addressed the challenge of generating indoor environments with semantic information. They proposed a comprehensive framework for constructing an enhanced map representation of the environment that includes object-level data. This representation can be utilized in various applications, such as visual navigation, assistive robotics, human-robot interaction, and manipulation tasks. Their approach involved instance semantic segmentation, localization, identification, and tracking of multiple object classes within the scene. This was achieved using a CNN-based object detector (Yolo) and a 3D model-based segmentation technique. To track and position semantic classes accurately, a dictionary of Kalman filters was employed, which combined sensor measurements over time. The formulation also accounted for recognizing and disregarding dynamic objects to provide a map representation that remains invariant over the medium term. The proposed method was evaluated using RGB-D data sequences recorded in different interior scenes, which were made publicly available. The experimental results demonstrated the effectiveness of the technology in generating augmented semantic maps, with a particular emphasis on the inclusion of doors. The authors released the source code as ROS (robot operating system) packages and provided a dataset containing annotated object classes (doors, fire extinguishers, benches, and water fountains) and their corresponding locations for the benefit of the research community. In [79], the authors emphasized the importance of autonomous robots adhering to prescribed travel paths, such as sidewalks or footpaths, while avoiding areas like grass and flowerbeds for safety and social reasons. They proposed a system that combines semantic segmentation from an RGB-D camera with LiDAR-based autonomous navigation for uncharted urban areas. Their method involved building a 3D map of the environment's obstacles using the segmented picture mask and calculating the limits of the footpath. The proposed approach eliminated the need for a pre-built map and provided a 3D understanding of the safe region for movement, allowing the robot to plan any route along the footpath. Comparative experiments demonstrated significant performance improvements over existing methods, with success rates exceeding 91% outdoors and over 66% indoors when compared to LiDAR-only or semantic segmentation-only approaches, respectively. The suggested strategy reduced collisions and ensured that the robot always followed the safest path. In [80], a human-robot interaction system for an indoor environment was proposed, utilizing visual and audio signals to create a grid-semantic map. The authors validated their work using four modules from the ROS development environment: the interaction module, control module, navigation module, and mapping module. The semantic map was generated through interaction between humans and robots using speech and visual signals. By having the robot follow the user and providing verbal commentary, a contextual topological map with semantic information was created.