# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 3 Collaborative Semantic Mapping
### Multi-Robot Semantic-Mapping Pipeline
#### 3.2.2 Distributed Semantic-Mapping Architecture  
While a centralized architecture is composed of agents and a server, the distributed architecture, shown in Figure 7, is composed only of a set of robots that work together to estimate the global map. Indeed, each robot runs its own semantic-mapping system. Then, the multi-robot data association and fusion process is performed by a single robot or by each member of the fleet depending on the application, the processing power of the robots and the communication constraints, in order to fuse the resulting individual semantic maps. This architecture is considered the appropriate technique when the bandwidth is limited and the robots have heterogeneous needs and capabilities [48], or when the application requires to maintain local autonomy for decision making (security, robustness, etc.). However, it is more challenging for inter-robot communication than the centralized architecture because the topology becomes unstructured, it can change dynamically depending on whether the other robots are in proximity or not, and also because of the data correlation problem that may occur in the case of a cyclic communication between robots. The main advantage of this architecture is that it ensures flexibility and autonomy of the robot fleet. Thus, in the case of a centralized architecture, when the server fails, all robots fail, whereas in a decentralized architecture, even if one robot fails, the other robots can continue their task using their own semantic maps and existing data from the other robots. Figure 7 shows the two possible formalizations of the semantic-mapping problem in a distributed architecture. In Figure (a)a, each robot builds its own local semantic map, and then receives the maps of its neighbors. Indeed, it receives data from robots with which communication is physically possible and within communication range at the time of multi-robot data gathering, to associate and merge them with its own map. In contrast, in the second scheme of Figure (b)b, each robot builds its own local semantic map, but only one robot collects all the maps of the neighboring robots and runs the data association and fusion algorithm to create the global map, and then sends it to the robots that are within its range and needed for their task. This case is very similar to the second formalization of the centralized architecture, where each robot creates its local semantic map, and then the fusion is executed in the server. The only difference here is that the task of data association and fusion is performed by a robot instead of the server. In general, if the robots in the fleet are not identical, the robot with the highest computational power performs this task. It is also possible to have a third option by combining the two schemes of the Figure 7, where each robot maintains a global map and the global maps are exchanged between the robots. In the literature, the majority of works that are interested in proposing a collaborative semantic-mapping system used a distributed architecture. For example, [PERSON] et al. [47; 50]  
Figure 7: Representation of the distribution of a semantic-mapping system in a distributed architecture. (**a**) Each robot collects data from neighboring robots and establishes data association and fusion to create its global semantic map, (**b**) a single robot collects maps from neighboring robots, creates the global semantic map and distributes it to other robots. used the Husky Clearpath mobile robots, equipped with powerful controllers. Each robot estimates its local map and updates it incrementally using the partial maps received from the other robots. In [51], 12 marine robots were used, where each robot creates its local semantic model of an ocean environment, and then a fusion algorithm is run by any robot in the fleet that has collected the maps from the other robots in order to fuse them into a global map.