# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### Hybrid Based Semantic Navigation Systems
In their work in [72], the authors introduced a modular system called \"Goal-Oriented Semantic Exploration\", which aimed to create an episodic semantic map and utilize it for efficient world exploration based on a specific target object category. The proposed system incorporates top-down metric maps that explicitly encode semantic categories. Rather than starting from scratch with first-person predictions, the system leverages pre-trained models for object detection and semantic segmentation to construct semantic maps, which are then combined with differentiable geometric projections. Furthermore, the authors developed a goal-oriented semantic exploration policy that learns semantic priors to facilitate effective navigation, instead of relying solely on coverage-maximizing and goal-agnostic exploration policies based on obstacle maps. Empirical results obtained in visually accurate simulation settings demonstrated that the proposed model outperformed various baselines, including end-to-end learning-based methods and modular map-based methods. Notably, the model achieved the highest performance in the CVPR-2020 Habitat ObjectNav Challenge, making it the winning submission. [PERSON] et al. [73] replaced the use of the occupancy grid map generated by SLAM with another type of map called the Layout map. The Layout map displays structures and contains semantic data, enabling the robot to find a route from its current location to the desired location. A layout map always depicts the entire area comprehensively and accurately. However, the authors noted that the Layout map may be inaccurate in terms of resolution, which could reduce the precision and reliability of navigation. To address this, the authors proposed calibrating the Layout map using the occupancy grid map to enhance navigation performance. They achieved this by using visible light positioning landmarks to determine the robot's location, reflecting location on the layout map as the robot underwent SLAM. Sensors were also used to locate the robot on the occupancy grid map. The experimental results demonstrated that the robot could navigate almost as well on calibrated floor maps as on calibrated building blueprints, and it performed better on calibrated layout maps than on sensor maps. In [74], the authors presented a robot navigation system that can perform navigation tasks in unfamiliar environments using the same symbolic spatial information used by humans. This includes labels, signs, maps, planners, spoken directions, and navigational gestures. The developed navigation system utilizes adaptable spatial models for hidden spaces based on spatial symbols, employing a novel data structure called the abstract map. A robotic-oriented grammar, with manually crafted clauses, is used to express the spatial information conveyed by navigation cues. The robot then uses its sensorimotor perceptions to navigate towards symbolic goal areas within the unknown surroundings. Overall, the abstract map helped the robot complete tasks 5.3% more quickly, excluding two outlier human results, and 11.5% faster than human participants. In their study [75], [PERSON] et al. utilized laser scanner raw data to extract semantic features, enabling object classification and the creation of semantic maps for building interiors. They introduced a semantic navigation scheme based on hypergraphs, which employed multiple layers to represent different levels of abstraction within the investigated building. Semantic information was obtained from the digital model of the building, such as the building information model (BIM), which adheres to the architecture-engineering-construction (AEC) industry standard [76] and describes the building's infrastructure. This semantic information facilitated robot navigation. The developed system incorporates advanced data processing techniques, including online updating of a 3D environment model, object classifiers based on rules and features, a path planner utilizing cellular neural networks, and other tools.