# Semantic Information for Robot Navigation: A Survey
## 3 Representation of Semantic Knowledge  
The literature offers different ways of representing high-level knowledge in navigation tasks. Semantic navigation approaches builds upon some of these knowledge representation paradigms. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Acquisition by Human Interaction**} & \multicolumn{2}{c}{**Autonomous Acquisition**} & \\ \hline
**Paper** & **Place Labeling** & **Learning Objects and Relationships** & **Laser Range Data** & **Segmentation** & **Object Detection** \\ \hline
[19] & YES & YES (BOTH) & NO & NO & YES \\
[15] & YES & YES (BOTH) & YES & YES & YES \\
[16] & YES & NO & YES & NO & YES \\
[17] & YES & YES (OBJECT) & YES & YES & YES \\
[74] & YES & NO & NO & NO & NO \\
[75] & YES & NO & NO & NO & NO \\
[21] & YES & DOORS & NO & NO & NO \\
[23] & NO & YES (OBJECT) & NO & YES & YES \\
[13] & NO & NO & YES & NO & YES \\
[24] & NO & NO & YES & NO & NO \\
[25] & NO & NO & YES & YES & NO \\
[28] & NO & NO & YES & NO & YES \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between information acquisition methods. This is the case of [PERSON] and [PERSON] that propose a spatial representation in a layered structure [17] as shown in Figure 3. This representation can describe usual knowledge as relationships between concepts (e.g., a kitchen _contains-the-object_ cereals). Additionally, instances of knowledge are described as relations between instances of concepts (object-\(1\)_is-an-instance-of_ cereals), or relations between instances and other instances (place \(X\)_has-the-object_ object Y). Relationships in the conceptual map can be predefined, acquired or inferred. These relations can also be deterministic or probabilistic (modelling uncertainty). This semantic navigation system bases inference on deductions on the unexplored space, such as predicting the presence of objects in a location with a known category. Additionally, this system is able to predict the presence of a room in unexplored space. Adding features to objects can also provide useful information since they can modify the functionalities of the objects [19]. For instance, a broken chair may not be used for sitting and be located in a workshop instead of in a living room. [PERSON] et al., presented one of the first works in the semantic navigation field, introducing multi-hierarchical models of representation [13]. This work proposed NeoClassic as a tool for representing knowledge and Descriptive Logics (DL) as inference mechanism [77]. The inputs to the classification system are outlines and, therefore, simple volumes, such as boxes or cylinders, represent furniture. Some authors include features of the environment such as the sound level or the number of people in each room [78]. Gaps authors left in their conclusions haven been covered, such as the problem of autonomously learning new properties and categories of rooms by the robot, heading to an auto extensible semantic mapping. [PERSON] et al., introduce a novel representation of a  
Figure 3: Spatial representation in a layered structure [17]. The conceptual layer contains concepts knowledge, relationships between concepts and spatial entities instances. semantic map called Multiversal Semantic Map [79]. The authors provide measures of uncertainty when they categorize an object into degrees of belief. An object can be labelled as a microwave with a certainty of 0.6 and as a bedside table with 0.4, for example. This type of semantic map contains all the categories considering all the possibilities. The work of [PERSON] et al. [13] was extended and different combinations of possible bases or universes were considered, such as instances of ontologies [80] with annotations of belief (certainty) about the concepts and relationships that are useful fundamentals. The potential of this representation was extended in subsequent works that implemented semantic navigation schemes. For example, the planner was improved in [PERSON] et al. [81] and a more recent work includes autonomous generation of objectives [82].