## 3 Marker-Assisted Region Scanning
### Update Robot State
A method that generates a complete map with a reduced number of poses demonstrates higher efficiency by avoiding redundancies and producing a more accurate and compact representation of the environment. As a result, the maps generated are more suitable for reuse and future applications since they contain sufficient data to efficiently represent the environment without compromising completeness. Thus, when evaluating the performance of MARS-SLAM, the number of poses becomes a criterion, reflecting the system's ability to create optimized, high-quality maps while reducing unnecessary data overhead. Table 6 presents the percentage variations in the number of poses when comparing different methods across three scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Map** & **Zigzag** & **ACO** & **MST** & **MSP** & **MSA** \\ \hline \multirow{3}{*}{\(\text{\textasciitifbox{100}}\)} & M1 & \(5.2\pm 0.2\) & \(1755.6\pm 62.5\) & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) \\  & M2 & \(126\pm 1.7\) & \(11458.6\pm 125.6\) & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) \\  & M3 & \(479.9\pm 2.6\) & \(118235.4\pm 12348\) & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) \\ \hline \multirow{3}{*}{\(\text{\textasciitifbox{100}}\)} & M1 & \(11571.1\pm 112.2\) & \(724.5\pm 207.7\) & \(33224.4\pm 76.9\) & \(3897.6\pm 962.5\) & \(5895.8\pm 1229.7\) \\  & M2 & \(3680.4\pm 180.1\) & \(931.5\pm 550.0\) & \(15,604.8\pm 2306.1\) & \(1091.0\pm 2185.5\) & \(1434.32\pm 4035.6\) \\  & M3 & \(12,320.5\pm 136.0\) & \(4942.2\pm 742.3\) & \(16,304.0\pm 3364.6\) & \(18,513.8\pm 2676.4\) & \(20,530.6\pm 2372.0\) \\ \hline \multirow{3}{*}{\(\text{\textasciitifbox{100}}\)} & M1 & \(1162.3\pm 112.4\) & \(2480.1\pm 270.2\) & \(3322.4\pm 766.9\) & \(3897.6\pm 962.5\) & \(5895.8\pm 1229.7\) \\  & M2 & \(3693.0\pm 181.8\) & \(12,3901.0\pm 675.4\) & \(15,604.8\pm 2306.1\) & \(10,910.9\pm 2185.5\) & \(14,342.0\pm 3452.6\) \\ \cline{1-1}  & M3 & \(12,368.4\pm 138.6\) & \(12,3177.6\pm 1977.0\) & \(16,304.0\pm 3346.4\) & \(18,513.8\pm 2676.4\) & \(20,530.6\pm 2372.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data showing the preprocessing time (_PT_), navigation time (_NT_), and total simulation time (_TT_) used by each method in the three maps, i.e., Zigzag, ACO, and three versions of MARS-SLAM, using three different selection methods: Tournament (MST), Proximity (MSP), and Age (MSA). Figure 7: Comparison between the methods for the number of poses (**a**) and total simulation time (**b**) across three different maps. ### Discussion

Overall, MARS-SLAM yields superior results, particularly when combined with the MST strategy, which reduces the number of poses by 64.39% compare to ACO and 71.07% compare to Zigzag in Map 3. This significant reduction reflects the method's efficiency in complex environments, such as Map 3, where the implementation of Zigzag and ACO requires the inclusion of a large number of markets. The increased number of markers made the optimization process too costly for ACO, which failed to find an efficient route within the simulation time limit. This reinforces the superiority of MARS-SLAM in handling complex environments, where its ability to reduce the number of poses translates into more efficient navigation and higher-quality mapping. A point to highlight is that MARS-SLAM is designed to operate in completely unknown environments, which significantly distinguishes it from the ACO and Zigzag methods. While these methods require prior knowledge of the map to distribute a marker grid that guides navigation, MARS-SLAM can autonomously explore and map unknown areas, adapting to real-time discoveries. This makes MARS-SLAM a more robust and versatile solution, as it does not depend on prior information or artificial setups to operate efficiently. Its ability to identify and explore unexplored regions adaptively, without the need for prior planning, positions it as a valuable tool for applications where the environment is entirely new or unpredictable.