# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 3 Collaborative Semantic Mapping
### Multi-Robot Data Association and Semantic Maps Fusion
#### 3.3.2 Fusion and Optimization
Then, in order to deal with the global deformation resulting from the inaccuracy of the location and orientation data provided by GPS (Global Positioning System) and AHRS (Attitude and Heading Reference System), a non-rigid point-set registration algorithm is used to estimate an affine transformation. Finally, the global semantic map is obtained by performing robust point-to-point registration on the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Ref.**} & \multirow{3}{*}{**Year**} & \multicolumn{3}{c}{**Sources of Dissimilarity**} \\ \cline{3-5}  & & \begin{tabular}{c} **Multiple** \\ **View-Points** \\ **Errors** \\ \end{tabular} & \
\begin{tabular}{c} **Annotation** \\ **Errors** \\ \end{tabular} &
\begin{tabular}{c} **Localization** \\ **Errors** \\ \end{tabular} \\ \hline
[52] & 2019 & X & X & X \\
[53] & 2019 & - & X & - \\
[47] & 2020 & X & - & - \\
[51] & 2021 & - & - & - \\
[49] & 2021 & X & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sources of dissimilarity addressed in the reviewed works in relation to the collaborative semantic-mapping framework. input point clouds using only the points belonging to the vegetation in the local semantic maps. In another work [53], the global semantic map is a topological graph connecting many robot sub-graphs. In this particular work, only semantic data is used to match the maps, so there are no errors caused by metric data, but there are dissimilarities caused by labeling errors, such as the association of a wrong label or the absence of a node. Finally, in addition to the automatic fusion systems described above, some works have proposed a system based on human intervention to optimize and maintain the resulting global representation. Indeed, in [49], since the robots map the environment at different times, the authors considered that the location of an object mapped by the first robot can be modified before the other robot maps the environment. This case is not solved by the automatic system designed to merge dissimilarities caused by multiple viewpoints. Therefore, they included the option for the operator to modify the final reconstructed labeled 3D map by replacing or deleting erroneous labels. It is also important to mention that some works do not focus on the fusion and optimization part but only on solving the data-association problem. For example, in [51], after clustering the topics associated with the unique phenomena explored in the ocean, the positions of the marine robots are considered error-free, so the fusion part consists only of concatenating the local semantic maps and segmenting the topics referring to the same phenomena with the same color. In conclusion, the fusion and optimization methods proposed in the literature mainly deal with multiple viewpoint errors and annotation errors in order to integrate local semantic maps into a global map. The majority of works consider that robot positions exist and attempt to optimize the detection positions by resolving inconsistencies in the corresponding maps. Meanwhile, few works take into account dynamic objects in the fusion process.