# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 2 Single-Robot Semantic Mapping
### Semantic-Map Representation
#### 2.2.2 Hierarchical Representations  
In order to allow robots to perform a reasoning process, more interesting representations have been proposed, namely, hierarchical representations, where specific information is placed in the lower levels and abstract information in the higher levels. [PERSON] et al. [31] proposed a representation, shown in Figure 4, that gives the robot the ability to infer the categories of places using the properties of the space, including appearance, surface, shape and objects. They represent the spatial knowledge by a four-layer hierarchical structure: the sensory layer, the place layer, the categorical layer and the conceptual layer. The lowest level of the structure contains sensor perception data and the fourth level contains abstract conceptual knowledge. The sensory layer contains a metric map of the environment. The place layer contains a topological graph with nodes representing locations and edges encoding the path to other nodes. The categorical layer contains objects, landmarks, and both geometric and visual models. Finally, the conceptual layer contains a static common-sense knowledge ontology and the links between the concepts in this ontology and the low-level knowledge obtained from the other three layers. This structure not only allows the robot to classify rooms, but also to predict the existence of objects, properties of the space and unexplored spaces. While the environment in [31] is represented by a single hierarchical structure, in [43], a representation with two hierarchical structures was proposed: the first to represent spatial knowledge and the second to represent conceptual knowledge. On one side, the spatial hierarchy is composed of three levels. The first and the lowest level contains local occupancy maps, locations and images, stored by the robot. The second level contains a topological graph of the environment. In addition, the third level contains an abstract node that represents the whole environment. On the other hand, the conceptual hierarchy is composed of four levels. It is hand-coded in advance with the NeoClassic language. The top level contains a node called \"_Thing_\" from which two branches, \"_Parts_\" and \"_Objects_\", emerge. The third level contains object and room categories, and the lowest level contains object and room instances recognized by the robot. These two hierarchies are linked by the anchoring process, which consists of linking the entities recognized at different levels of the spatial hierarchy to the corresponding symbols that represent them in the conceptual hierarchy. These links allow the robot to exploit semantic information to determine localization errors by reasoning of the objects' expected locations and to perform semantic navigation tasks. Similarly, in [40], the robot knowledge is divided into two parts: world knowledge, which represents the specific knowledge of a certain environment, and domain knowledge, which is the general knowledge of the application domain. The world knowledge is composed of the following elements: an occupancy grid, cell maps to represent local locations, a topological graph of the global environment and instances of recognized physical elements with their categories and properties. On the other hand, the domain knowledge is composed, as in [43], of the concepts involved in the environment as well as their properties and relationships. For this work, the main purpose of adding domain knowledge is to associate each perceived object with its spatial and functional properties in order to allow the robot to reason about the tasks related to these objects. Figure 4: The layered structure of the spatial representation and a visualization of an extract of the conceptual-layer ontology proposed in [31]. In conclusion [12], the representations that consist of visualizing the semantic information by different colors on the map allow to show results of semantic knowledge acquisition, but they are not easily implemented in the robots' tasks.