# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 3 Collaborative Semantic Mapping
### Multi-Robot Semantic-Mapping Pipeline  
The multi-robot semantic-mapping system is mainly composed of the same main modules as the single-robot semantic-mapping system (Section 2). First, the geometric mapping module is in charge of creating the geometric representation of the environment. Then, the semantic segmentation or object-recognition module is in charge of understanding the environment and collecting semantic data. Last, the map-representation module is in charge of creating and updating the model of the environment. Nevertheless, the collaborative semantic-mapping system includes an additional module, which can be called the data association and fusion module. It treats the problem of associating and fusing multi-robot data into a coherent global model. In addition, this system is generally characterized by three main features: the task and data distribution scheme, the communication policy and the multi-robot data association and fusion strategy. The task and data distribution scheme:defines the system architecture, its components, the connections between them and the tasks assigned to each component. Figure 5: An example of a collaborative robotics setting. using their sensors, and then send them to the server for processing and estimation of the global semantic map. More specifically, all the single-robot semantic-mapping modules, of each robot, are implemented in the server, mainly the SLAM task and the semantic data acquisition task. In addition, the server determines the correspondences between the data of different robots and fuses them in order to maintain a consistent global semantic map. In this case, the robots are very dependent on the server and have a low degree of autonomy. If the server or communication breaks down, the robot loses contact with the other robots. In the second case, a complete semantic-mapping system is implemented in each robot, as described in Section 2. Then, the robots communicate their individual semantic maps to the server where only the inter-robot tasks are performed, namely, the multi-robot semantic-map association and fusion. In this case, the knowledge database is removed from the inputs of the single-robot semantic-mapping system and generally implemented in the server to be shared among all robots. This formalization makes the robots more autonomous, as they always have their own representations in case of server or communication failures. However, in some cases, the robot does not build a complete individual semantic map. Indeed, it runs basic modules such as the SLAM algorithm to collect geometric data and an object-recognition or semantic-segmentation algorithm to collect semantic data, but it does not create a local semantic map. Then, the data exchanged here is coupled with semantic and geometric information and not local maps. Therefore, the multi-robot data association and fusion module, implemented in the server, must process the correspondences and inconsistencies of the incoming data from several robots and integrate them into a single representation of the environment (Section 3.3.2). Considering these formalizations, we can conclude that three types of data can be communicated by the robots to the server: sensor row data, separate geometric and semantic data, and local semantic maps. Therefore, each case has its specific requirements in terms of communication bandwidth latency and robot processing power. In the literature, there are a few works that use a centralized architecture for semantic mapping, which covers the second case presented in Figure 6b. In this work [49], the authors proposed a server-client architecture for mapping a domestic environment. On the one hand, the client implements four main components: an object-recognition module, an object information-processing module, a robot-localization module and a communication module. Indeed, the object-recognition module is used to detect the robot's environment and the objects within it.