## 3 Marker-Assisted Region Scanning
### Update Robot State
In terms of completeness, as presented in Table 4, all methods are able to fully map the environment, with minor variations in Map 3, where the complexity of the environment led to a slight reduction in mapping completeness for MST (99.8%) and MSP (99.5%). Among the 30 simulations conducted with MARS-SLAM on Map 3 (10 simulations for each strategy), in 2 of them, the method did not achieve 100% completeness. This occurs due to the specifics of the marker placement methodology, referred to as the shadow effect. As illustrated in Figure 8, the shadow effect occurs due to a combination of factors related to the LiDAR's functionality and the marker placement methodology in the system. This effect happens when the robot, while navigating through dense or narrow regions, fails to add markers correctly in all unmapped areas. As a result, the robot perceives some unexplored areas as already mapped, since the absence of markers indicates to it that the area has been explored. This issue is partly caused by the limited reading angle of the LiDAR, which only performs measurements from \(-90^{\circ}\) to \(90^{\circ}\), along with the strategy of adding new markers only at the limits of the LiDAR's range when it detects no obstacles. In narrow environments, such as corridors or doorways, the robot may pass by a structure without successfully adding markers within the passage, as the LiDAR, depending on the robot's orientation, may not capture the complete area (such as a narrow entrance). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & \multicolumn{6}{c}{**The Number of Poses Variation (\%) Compare To**} \\ \cline{3-7}  & & **Zigzag** & **ACO** & **MST** & **MSP** & **MSA** \\ \hline \multirow{4}{*}{
\begin{tabular}{l}



For example, when passing perpendicularly to a narrow doorway, the robot may only detect the doorframe and not \"see\" the inner space, which prevents markers from being placed in that area. This effect is also intensified in maps with dense structures, where marker dispersion is high and insufficient to cover all spaces, leading to a false perception of mapping completeness. The shadow effect represents a critical challenge for complete mapping accuracy in MARS-SLAM, revealing the need for further investigation and improvements in the marker placement methodology. This phenomenon, which occurs mainly in narrow areas poorly detected by the LiDAR, indicates the need for adjustments in sensor configuration or complementary strategies to ensure these shadowed regions are properly mapped. Measures such as increasing marker density in more complex areas or adjusting LiDAR parameters could mitigate this effect and improve mapping completeness. Moreover, despite the overall good performance of MARS-SLAM, particularly when compared to ACO and Zigzag, in more complex scenarios like Map 3, the challenges encountered in environments with many narrow zones or sharp angles highlight the difficulties in accurate marker placement. In these cases, the method may struggle with precise marker positioning, which in turn increases processing time and the complexity of navigation. Although MARS-SLAM still maintains a significant advantage over alternative approaches, these factors demonstrate the importance of enhancing the method to handle scenarios with low visibility or high obstacle density. ## 6 Conclusions

This paper proposed MARS-SLAM, a novel method that optimizes the Simultaneous Localization and Mapping process by leveraging markers to enhance navigation efficiency in unknown environments. Experimental results in three distinct virtual scenarios demonstrated that MARS-SLAM, particularly when combined with the MST strategy, significantly outperforms traditional approaches such as Zigzag and ACO. Notably, in the most complex scenario (Map 3), MARS-SLAM reduced the number of poses by 71.07% compared to Zigzag and 64.39% compared to ACO, highlighting its efficiency in minimizing redundant movements while maintaining mapping accuracy.