# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 3 Collaborative Semantic Mapping  
Recently, collaborative robotics--which involves multiple agents, including robots, smart machines, and operators, working together to accomplish a task, as presented in Figure 5--has gained increasing interest in the research community. This collaboration provides a great opportunity to improve the performance of a fleet of robots. Indeed, at the individual level, it allows to increase the accuracy and robustness of a robot's estimations by integrating data collected by other agents and also to reduce its computing resources by distributing and parallelizing tasks. At the second level, it improves the efficiency of the fleet by task parallelization and by allowing the robots to substitute each other in case of a robot failure. However, on the other hand, this introduces some complexity in robotic systems and presents new challenges, especially in terms of inter-robot communication \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Semantic Data**} & \multicolumn{2}{c}{**Collected Semantic Data**} \\  & & **Sources** & **Approach** & **About Objects** & **About Places** \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{2008} & Perception - HRI & Vision-based SLAM, & Categories, & Categories, \\  & & Reasoning & object recognition & instances & instances \\ \hline \multirow{3}{*}{[31]} & \multirow{3}{*}{2012} & \multirow{3}{*}{2012} & \multirow{3}{*}{\begin{tabular}{c} Perception - HRI \\ Reasoning \\ \end{tabular} } & \begin{tabular}{c} EKF-SLAM, \\ recognition and instance reasoning, \\ properties classification \\ \end{tabular} & \begin{tabular}{c} Categories, \\ relationships with other \\ concepts \\ \end{tabular} & \begin{tabular}{c} Appearance, \\ surface, shape \\ \end{tabular} \\ \hline \multirow{3}{*}{[40]} & \multirow{3}{*}{2013} & \multirow{3}{*}{HRI} & Incremental approach by HRI & \begin{tabular}{c} Categories, positions, \\ sizes, properties \\ \end{tabular} & \
\begin{tabular}{c} Categories, \\ robot positions \\ \end{tabular} \\ \hline \multirow{3}{*}{[3]} & \multirow{3}{*}{2015} & \multirow{3}{*}{HRI} & RBPF-SLAM, voice recognition & \multicolumn{1}{c}{–} & Categories \\ \hline \multirow{3}{*}{[27]} & \multirow{3}{*}{2016} & \multirow{3}{*}{Perception} &
\begin{tabular}{c} Monocular LSD-SLAM, \\ 2D segmentation with CNN \\ \end{tabular} & Categories & – \\ \hline \multirow{3}{*}{[41]} & \multirow{3}{*}{2017} & \multirow{3}{*}{Reasoning} & \multirow{3}{*}{–} & Instances, categories, \\  & & & utilities, characteristics, \\ \cline{1-1}  & & & relationships with places & categories \\ \hline \multirow{3}{*}{[29]} & \multirow{3}{*}{2017} & \multirow{3}{*}{Perception} & RGBD-based ORB-SLAM2, \\  & & Object Detection, \\ \cline{1-1}  & & 3D Segmentation with CNN & \\ \hline \multirow{3}{*}{[4]} & \multirow{3}{*}{2020} & \multirow{3}{*}{Perception} & Sonar-based SLAM, object detection, \\ \cline{1-1}  & & & triangulation \\ \hline \multirow{3}{*}{[45]} & \multirow{3}{*}{2022} & \multirow{3}{*}{Perception–reasoning} & \multirow{3}{*}{Monocular SLAM, object detection} & Categories, \\ \cline{1-1}  & & & relationships with other \\ \cline{1-1}  & & & concepts \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the reviewd works in relation to the single-robot semantic-mapping framework. and multi-robot data processing [46]. For example: _data exchange, data association and data representation in a global map._  
In this context, over the past few years, a few researchers have started working on the multi-robot semantic-mapping problem, where a fleet of robots work together, while exchanging data, to create a global semantic map of the environment. These works are based on the progress made in various previous works on single-robot semantic mapping and take advantage of the advances made in the field of collaborative robotics, such as inter-robot communication methods and different data-allocation architectures to propose new consistent collaborative semantic-mapping approaches [47].