# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 2 Single-Robot Semantic Mapping
### Data Acquisition and Processing
#### 2.1.2 HRI-Based Data Acquisition  
According to [3], the automatic extraction of semantic information from sensors is limited and not robust enough. Indeed, object and place recognition are complex tasks for robots. That is why some techniques integrate the human in the process of identifying objects or places in order to create an augmented map [11; 12].  
In [3], the authors guided the robot through a tour of the environment to map it. The RBPF-SLAM algorithm [37; 38] based on laser data and odometry data was used to generate an occupancy map in real time. At the same time, the user has an IFLYTEK-based voice application on their cell phone to label the visited places on the map. In order to avoid errors in the recognition of voice commands given by the operator, a confirmation step is applied. In the same way, in [23], the robot has a prior knowledge of spatial concepts, and the user's role is to assist it in the process of labeling places. Indeed, while walking with the robot, the user expresses what they considers relevant, for example: _\"This is the corridor\"_ or _\"This is the charging station\"_.  
In another approach [39], as shown in Figure 2, the robot is guided by the user in a tour of the operational environment. It perceives its environment and detects the object that the user is pointing to with a commercial laser. Then, this object is segmented in the image and the robot estimates its position and orientation. Finally, it receives its description from the user via a speech-recognition module and associates it with its position to build the object representation to be added to the map. In [40], [PERSON] et al. improved this approach by implementing a system that enables adding new objects to the representation through continuous and online interaction with the user after the initial semantic mapping. Indeed, knowledge is acquired as needed by the robot and added progressively to the environment representation. For example, if the user gives a voice command indicating an unknown location for the robot, an integrated Petri net acquisition process is launched. During this process, the user can guide the system with voice commands such as _\"Turnright\"_, \"_Follow me\"_ or \"_Go to the kitchen\"_. When the robot is in front of the new location or object, the user can point to the object with the laser then tell the robot its label, e.g., \"_This is the emergency door_\".  
[PERSON] and [PERSON] [31] proposed an algorithm that combines information about the presence of objects with the semantic properties of places, such as size and appearance, to classify rooms. During the mapping process, the user can input additional information about objects in the room using a graphical interface on the computer used by the robot. For example, if the user provides information about the existence of an object, the robot treats it as an additional source of information. The work of [PERSON] et al. [41] implemented natural language dialogues between the user and the robot through a voice interface and the keyboard. Therefore, it can add object categories and their semantic relations to the map. For example, the robot can ask the user about the possible uses of an object or about the possibilities of interaction with objects in the environment.  
In conclusion, using HRI-based methods, the map representation can be enriched with various semantic information. Thus, by interacting with the robot via a mobile application, voice commands, or a user interface, an operator can include information about the objects in the scene (the categories of objects, the space they occupy, and their presence in a room), information about the locations (the categories of rooms and their characteristics), and information about the semantic relationships between the objects (the utility of an object). Moreover, while most works focus only on mapping static scenes, few works propose HRI-based data acquisition techniques to update the representation of dynamic features [40].