# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 2 Single-Robot Semantic Mapping
### Data Acquisition and Processing
#### 2.1.3 Reasoning-Based Semantic Data Acquisition  
Reasoning is a way that allows the robot to acquire significant knowledge of its environment. It consists of using data already obtained by other methods and a knowledge database to infer new information about the environment. In general, the knowledge database contains common-sense knowledge, that is, knowledge that all humans possess and that they have acquired since their birth without even being aware of it [42]. This database is usually represented as a structure composed of concepts describing the environment linked by rules. For example, [PERSON] et al. [43] proposed a hierarchical structure of conceptual information modeled by the NeoClassic language [44]. The lowest level of this structure is composed of symbols related to physical elements detected in the environment. The conceptual structure is composed of laws that link these symbols to concepts and link concepts together to allow reasoning about symbols of known categories. For example,  
Figure 2: Adding semantic data to the map by tagging objects with a comercial laser pointer and giving a natural language description [40]. (**a**) The laser pointer is used to mark the object. (**b**) The point is detected in HSV color space. (**c**) The object is located in reference to the robot laser scan (represented by black pixels). if a detected physical element is related to the symbol \"_oven-1_\", and the symbol is related to the concept \"_oven_\", and the concept \"_oven_\" is related to the concept \"_kitchen_\" by a link \"_contains_\", then the robot's current location can be concluded as the kitchen. In the case of [23], common-sense knowledge about an indoor office environment is modeled in the ontology shown in Figure 3a. An ontology is a data model representing a set of concepts in a domain and relationships between them. Based on this description, the system uses a reasoning software to derive more specific categories for known topological areas. Indeed, as shown in Figure 3b, if an area is classified as a room (\"_area1_\") and it contains a coffe machine, thus, using the conceptual knowledge given by the ontology, this area can be classified as a kitchen instance. Similarly, a semantic ontology is used in [45] to create a task scheduling strategy for service robots. In this ontology, the concept \"_objects_\" is separated into \"_static objects_\" and \"_dynamic objects_\". Then, the static objects are linked to the dynamic objects by probabilistic relations which allow to obtain the approximate positions of the dynamic objects by reasoning. Furthermore, the authors of [31] propose a probabilistic conceptual structure where the rules linking the concepts can be predefined, acquired or inferred and can be probabilistic or not. The categories and properties of elements in the environment are estimated together using a probabilistic chain graph model. Room properties such as objects, appearance, area, and shape are extracted from sensors, then used with room-object relationships provided by the Open Mind Indoor Common Sense knowledge database to infer room categories. The authors of [41] proposed a relational model that allows to easily store information about the environment in tables managed by a reasoning engine. The design of this model implicitly defines the relationships between entities. Therefore, it is not necessary to manually define rules between them as for other structures. Using this model, if the robot identifies objects in the same room that have a common utility, it is possible to infer that these objects are located in a room where the robot could find other objects with related utilities. Thus, new room categories are created autonomously. In addition, the identification of objects and rooms allows the detection of possible inconsistencies. Figure 3: The example of reasoning-based data acquisition in [23]. (**a**) Illustration of a part of the common-sense ontology of an indoor office environment, (**b**) combining different types of knowledge in the conceptual map.