# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 3 Collaborative Semantic Mapping
### Multi-Robot Data Association and Semantic Maps Fusion
#### 3.3.1 Data Association
**Grouping labels referring to the same element:** In this third category, the main objective of the data association is to group together the labels develeped by different robots and referring to the same physical element of the environment. For example, this method is used in [49], where the authors proposed a server-client system to build and maintain a semantic map of a dense domestic environment. This system generates a 3D environment reconstructed under Unity 3D, where each detection is added as a label to the virtual environment and linked to a semantic concept in an ontology. In effect, each robot processes its detections to determine object categories, 3D positions, dimensions and confidence scores. Then, it sends these data with its position to the server. The latter first determines the global positions of all detected objects in the reference system of the virtual environment. Then, it establishes multi-robot data correspondences using the physics provided by Unity 3D. It actually detects when virtual objects are close to each other or even in contact with each other with respect to a distance threshold. In the case of a collision, if they also share the same labels, then they are considered to refer to the same object. This method of data association is quite simple, but its main difficulty lies in the definition of the threshold distance allowing to consider the existence of a collision or not, because the objects in the environment have variable sizes. Thus, in some cases, the distance between two labels can be large enough to consider that they refer to two distinct objects, while in reality they refer to the same large object. In the same spirit, the data-association method proposed in [51], consists of estimating the number of unique phenomena developed by different marine robots after completely exploring their area of the environment. As shown in Figure 9, each robot develops its individual semantic model of the environment, and then the correspondences between them are resolved into a global semantic map. While in [49], a pre-trained deep-learningmodel is used for semantic mapping, so that the labels are known; in this work, the online unsupervised BNP-ROST model [56] is used, so that the detected features are developed as topics without associated labels (Topic 1, Topic 2,...) and are different from one robot to another. To establish the data association, the designed algorithm uses the final models of all robots to compute a topic similarity matrix to identify when the robots have developed semantically equivalent topics. Then, noisy topic similarities are removed in a two-step process. In the first step, similarity weights below a defined threshold are removed to obtain the noisy association graph. Then, in the second step, the obtained noisy association graph is rectified using the CLEAR algorithm [62] to generate the final cluster graph, where the topics belonging to the same cluster represent a unique phenomena. This method is very interesting for collaborative exploration of unknown environments, but it is highly dependent on the performance of individual robot models, which do not require pre-training, but need to be well-tuned by many varying parameters depending on the mission, which is not such a simple task.