# 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  
The data-association techniques proposed in the reviewed papers are designed according to the type of semantic map generated by the single robot. Indeed, the local map type defines the kind of correspondences to be established between these maps. Moreover, these techniques depend on the characteristics of the mapped environment, such as whether it is dense or flat, static or dynamic, and feature-rich or feature-less. For example, it is easier to match data when the environment contains distinctive features than when it contains similar repeating patterns. Below, we classified the techniques used for semantic-map association into three main categories: metric-semantic maps matching, topological-semantic maps matching, and grouping data referring to the same element. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Fusion Mode**} & \multirow{2}{*}{**Input Data**} & \multicolumn{2}{c}{**Data Association and Fusion Methods**} & \multirow{2}{*}{**Map Representation**} \\ \cline{3-4} \cline{6-6}  & & & **Data association** & & & \\ \hline
[52] & 2019 & Incremental & \
\begin{tabular}{c} Local grid-based \\ multi-modal maps \\ \end{tabular} & Point-to-point matching & Map alignment &
\begin{tabular}{c} Global grid-based \\ multi-modal map \\ \end{tabular} \\ \hline
[53] & 2019 & One-shot & Local OSTM [54] & Graph matching & Graph merging & Global OSTM \\ \hline
[47] & 2020 & Incremental & 3D Octree local maps [55] & Voxel-to-voxel matching & Bayesian fusion &
\begin{tabular}{c} 3D Octree global \\ map \\ \end{tabular} \\ \hline
[51] & 2021 & One-shot & \begin{tabular}{c} BNP-ROST unsupervised \\ learning models [56] \\ \end{tabular} & \begin{tabular}{c} Matching topics developed in \\ individual robot models \\ \end{tabular} & \
\begin{tabular}{c} Associating a single \\ label to similar topics \\ \end{tabular} &
\begin{tabular}{c} Global semantic \\ occupancy model \\ \end{tabular} \\ \hline
[49] & 2021 & Incremental & \begin{tabular}{c} Coupled geometric \\ and semantic informations \\ \end{tabular} & \
\begin{tabular}{c} Grouping labels associated with \\ the same physical element \\ under a common virtual object \\ \end{tabular} &
\begin{tabular}{c} Assigning a parent label to \\ each virtual object and \\ linking it to the ontology \\ \end{tabular} & Labeled 3D virtual \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different data association and fusion techniques used in collaborative semantic mapping. **Metric-semantic maps matching:** This category includes methods where the data-association problem is to match semantic-occupancy maps, specifically by determining point-to-point correspondences in the case of 2D local maps and voxel-to-voxel correspondences in the case of 3D local maps. For example, [PERSON] et al. [47] focused on matching 3D semantic-occupancy grid maps to construct a global map of an indoor-outdoor environment. They consider that existing works using only geometric data, such as planes, lines and points, to establish data association between local maps are not very effective in feature-less environments. Therefore, they propose to match local maps using both semantic and geometric data. To this end, as shown in Figure 8, each robot follows a trajectory and performs a semantic-mapping system proposed by [55] in a part of the environment. Then, the generated local semantic maps, which are 3D occupancy maps divided into voxels (3D Octree maps), are merged. Indeed, each of these voxels has an occupancy probability and a semantic-class probability computed by applying the Bayes rule. In order to determine the data association, each robot receives the maps of the neighboring robots according to a certain permutation and calculates the relative transformation matrix between the received map and its own map using the matching algorithm proposed in [50]. Then, the expectation-maximization (EM) algorithm [57] is used to infer the hidden data association.