## 2 Related Works
### SLAM Optimization Approaches  
In [32], the authors introduce the P-SLAM algorithm, which enhances the efficiency of mobile robot exploration and mapping through the prediction of environmental structures in unexplored regions. By using an environmental structure predictor, P-SLAM generates hypotheses based on observations from neighboring explored regions and compares them with the constructed map, allowing mobile robots to decide whether or not to explore those areas, saving time. The implementation of P-SLAM with a Rao-Blackwell particle filter showed effectiveness in indoor environments, reducing exploration time and speeding up the SLAM process. The Bayesian formulation derived from P-SLAM demonstrates its viability for real-time operations.  
In [33], the authors develop an approach for exploring unknown environments using an exploration EM algorithm adapted to the context of pose SLAM, without the need for explicit feature modeling. The proposed framework uses point cloud segmentation to improve place recognition and virtual map construction, allowing the prediction of the uncertainty resulting from future sensing actions. The research identifies the need to improve path primitives, which are currently limited to frontier locations, to mitigate the increase in uncertainty during exploration, pointing to promising directions for future work.  
In [34], the challenges of autonomous exploration and map building for mobile robots, especially in unknown environments, are addressed. The study presents a new boundary exploration method using a Double-Layer Rapid-Exploration Random Tree (DL-RRT), combined with Affinity Propagation (AP) clustering and Bayesian Optimization (BO).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Feature** & [29] & [32] & [30] & [33] & [31] & [34] & [35] & **MARS-SLAM** \\ \hline Indoor Environment & ✗ & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ & ✓ \\ Outdoor Environment & ✓ & ✗ & ✗ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Path Planning & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Dynamic Environments & ✗ & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ \\ Exploration Optimization & ✓ & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ & ✓ \\ Real-Time Processing & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ & ✓ \\ Conclusion Detection & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ \\ Map Building & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ Uncertainty Reduction & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ & ✗ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics’s comparison addressed in related works, where ✗ indicates the absence of a feature and ✓ denotes its presence.  
The DL-RRT method enhances the exploration of boundary points both globally and locally, while AP clustering optimizes the selection of boundary points by grouping and reducing their number. BO further refines exploration by prioritizing the grouped points based on desired parameters.  
In [35], an approach for 3D environment exploration by unmanned ground vehicles (UGVs) is presented. The proposed method, called FIT-SLAM, combines Fisher information and navigability estimates to optimize the exploration rate and SLAM (Simultaneous Localization and Mapping) accuracy. The method suggests the use of a global navigability map that considers environmental constraints and a goal selection and path planning method that incorporates information from the landmarks detected by SLAM. The study shows that by transforming the 3D exploration space into a 2D navigability map, FIT-SLAM significantly reduces the time to search for frontier clusters and the computational cost associated with 3D voxel mapping, while also improving the exploration rate and localization accuracy.