## 3 Marker-Assisted Region Scanning
### Update Robot State
Map 1 presents a simple layout, Map 2 has intermediate complexity, and Map 3 is the most challenging, with narrow areas and multiple obstacles. period in which the robot is actively exploring the environment and mapping unknown regions. To assess the efficiency of the marker selection method, three distinct versions of MARS-SLAM are tested, each using a different marker selection method: Tournament (MST), Proximity (MSP), and Age (MSA). This approach aims to determine whether the selection method impacts the effectiveness of MARS-SLAM, allowing a comparative analysis of the results obtained with each selection strategy. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **Map** & **Zigzag** & **ACO** & **MST** & **MSP** & **MSA** \\ \hline \multirow{3}{*}{\(\approx\)} & M1 & 13,070.4 \(\pm\) 102.4 & 7742.7 \(\pm\) 107.7 & 11,370.2 \(\pm\) 1177.7 & 10,9520 \(\pm\) 436.7 & 17,536.5 \(\pm\) 10,540.7 \\  & M2 & 39,963.9 \(\pm\) 166.6 & 18,760.6 \(\pm\) 145.5 & 26,983.3 \(\pm\) 3886.4 & 24,343.1 \(\pm\) 1300.2 & 32,106.3 \(\pm\) 1130.2 \\  & M3 & 18,2458.1 \(\pm\)646.9 & 14,8219.2 \(\pm\)1177.4 & 54,883.1 \(\pm\)1491.9 & 52,790.0 \(\pm\) 645.0 & 59,284.6 \(\pm\)1184.1 \\ \hline \multirow{3}{*}{\(\approx\)} & M1 & 100.0 \(\pm\) 0 & 100.0 \(\pm\) 0 & 100.0 \(\pm\) 0 & 100.0 \(\pm\) 0 & 100.0 \(\pm\) 0 \\  & M2 & 100.0 \(\pm\) 0 & 100.0 \(\pm\) 0 & 100.0 \(\pm\) 0 & 100.0 \(\pm\) 0 & 100.0 \(\pm\) 0 \\  & M3 & 100.0 \(\pm\) 0.0 & 100.0 \(\pm\) 0 & 99.8 \(\pm\) 0.5 & 99.5 \(\pm\) 1.4 & 100.0 \(\pm\) 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data showing the number of poses (\(NP\)) and mapping completeness (\(MC\)) 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 6: Final mapping results (100% completion) using the three methods (Zigzag, ACO, and MARS-SLAM) on three different maps. The red line represents the path taken by the robot. In the Zigzag and ACO methods, the red points indicate the markers used to construct the planned routes. To view the simulation videos, visit the YouTube playlists: Map 1 ([[https://www.youtube.com/playlist?list=PLJzSamlKHHmIBUAraCmh2](https://www.youtube.com/playlist?list=PLJzSamlKHHmIBUAraCmh2) obtFYAUhLN0]([https://www.youtube.com/playlist?list=PLJzSamlKHHmIBUAraCmh2](https://www.youtube.com/playlist?list=PLJzSamlKHHmIBUAraCmh2) obtFYAUhLN0)), Map 2 ([[https://www.youtube.com/playlist?list=PLJzSamlKHHmIKY1](https://www.youtube.com/playlist?list=PLJzSamlKHHmIKY1) uH8 QDqK_tTexXWEYEj]([https://www.youtube.com/playlist?list=PLJzSamlKHHmIKY1](https://www.youtube.com/playlist?list=PLJzSamlKHHmIKY1) uH8 QDqK_tTexXWEYEj)), and Map 3 ([[https://www.youtube.com/playlist?list=PLJzSamlKHHmIKA6](https://www.youtube.com/playlist?list=PLJzSamlKHHmIKA6) dYu6 si1 FDIAuL4 POUs]([https://www.youtube.com/playlist?list=PLJzSamlKHHmIKA6](https://www.youtube.com/playlist?list=PLJzSamlKHHmIKA6) dYu6 si1 FDIAuL4 POUs)), all accessed on 16 March 2025. Figure 7 illustrates the comparison between the evaluated methods based on two criteria: the number of poses and the total simulation time. Figure 6(a) compares the number of poses used by each method across the three maps; Figure 6(b) compares the required simulation time. It is observed that for Maps 1 and 2, MARS-SLAM demonstrate consistency, showing a number of poses similar to the comparative methods. However, in Map 3, MARS-SLAM achieves significantly superior performance, using fewer poses, which validates its effectiveness in complex unknown environments. Although simulation time is an important factor as it directly impacts resource costs during the SLAM process, it can be managed or even justified in many applications. On the other hand, the number of poses stands out as a more relevant indicator of the quality of the final mapping.