# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 4 Experiments
### Analysis of Experimental Results
#### 4.2.4 Result4: Ablation Experiment  
**(1) Ablation Study on Different Observation Modules**  
To verify the effectiveness of the Mamba historical observer, an ablation experiment was designed in this section, consisting of three parts: the no-agent module, the Mamba observation module, and the observation module combined with different reinforcement learning algorithms and the Attention mechanism. Partial experimental results are presented in Table 7.  
The data in Table 7 demonstrate that the proposed method achieves an optimal trade-off between localization accuracy and the real-time performance of the SLAM system. In the absence of the Mamba module, the DQN method achieves a lower RMSE of 0.013 on the MH01 dataset compared with the agent-free method, which has an RMSE of 0.016. However, there is no significant improvement in computational time. \begin{table}
\begin{tabular}{c c c c} \hline  & **ORB\_SLAM3** & **DDPG\_SLAM [6]** & **Ours** \\ \hline MH01 & 199 & 223 & 220 \\ MH02 & 162 & 183 & 163 \\ MH04 & 106 & 118 & 107 \\ V202 & 123 & 137 & 123 \\ Corridor2 & 366 & 381 & 373 \\ Corridor5 & 344 & 349 & 349 \\ Room2 & 158 & 171 & 158 \\ Room5 & 154 & 168 & 156 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of partial sequence system running time. \(Second/s\downarrow\). \begin{table}
\begin{tabular}{l c} \hline \hline
**Agent Type** & **Memory Usage (MB)** \\ \hline DQN with Mamba & 728.4 \\ DQN Without Mamba & 529.6 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of GPU memory usage. Upon incorporating the Mamba module, our method shows superior performance across multiple datasets. On the MH01 dataset, the RMSE of our method is 0.007, which is comparable to that of A3C+Mamba (0.007) but with a slight advantage in computational time. On the V202 dataset, our method achieves an RMSE of 0.019, slightly better than A3C+Mamba's 0.020, with identical computational time. When compared with the \"DQN + Attention\" methods, including DRQN and DQN+Transformer, our method outperforms both in terms of localization accuracy and runtime. For instance, on the Room5 dataset, the RMSE of our method is 0.009, significantly lower than DRQN's 0.010 and DQN+Transformer's 0.024, while the runtime is identical to that of DRQN. In conclusion, the proposed method ensures high localization accuracy while significantly improving the real-time performance of the SLAM system, demonstrating stronger adaptability and superior performance compared with existing approaches. Figure 7 shows the trajectory comparison of different agent methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Agent** & \multicolumn{2}{c}{**MH01**} & \multicolumn{2}{c}{**V202**} & \multicolumn{2}{c}{**Corridor2**} & \multicolumn{2}{c}{**Room5**} \\ \hline  & **RMSE** & **Time** & **RMSE** & **Time** & **RMSE** & **Time** & **RMSE** & **Time** \\ \hline \multicolumn{8}{c}{**Without Mamba**} \\ \hline - & 0.016 & **220** & 0.029 & **123** & 0.040 & 374 & 0.020 & **154** \\ DQN & 0.013 & 221 & 0.029 & 124 & 0.014 & 375 & 0.026 & 156 \\ \hline \multicolumn{8}{c}{**DRL + Mamba**} \\ \hline DDQN + Mamba & 0.015 & 224 & 0.021 & **123** & 0.012 & 374 & 0.013 & 156 \\ A3C + Mamba & **0.007** & 228 & 0.020 & 128 & 0.010 & 375 & **0.008** & 157 \\ PPO + Mamba & 0.010 & 232 & **0.018** & 132 & **0.009** & 384 & 0.009 & 158 \\
**Ours** & **0.007** & **220** & 0.019 & **123** & 0.011 & **373** & 0.009 & 156 \\ \hline \multicolumn{8}{c}{**DQN + Attention**} \\ \hline DRQN & 0.012 & 242 & 0.020 & 134 & 0.013 & 395 & 0.010 & 169 \\ DQN + Transformer & 0.008 & 252 & 0.020 & 146 & 0.015 & 400 & 0.024 & 177 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation Comparison experiment on different agents. \(RMSE\) and \(Time\downarrow\). Figure 7: Comparison of trajectories from different methods on the MH01 sequence. The red line represents the trajectory of the Mamba-DQN observed agent, the green line represents the DQN agent trajectory, and the blue line represents the original ORB-SLAM3 trajectory.