# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 5 Conclusions  
The SLAM method proposed in this paper incorporates a multi-scale information extractor and full-attention cross-modal feature fusion technology, which has shown remarkable performance in both qualitative and quantitative experiments. In qualitative experiments, the estimated trajectory closely matches the ground truth, highlighting the high precision of the multi-scale information extractor in capturing camera pose information. For semantic mapping, the full-attention cross-modal feature fusion enables the algorithm to fully exploit features at various levels, capturing object details and making effective inferences based on existing information. Quantitative and ablation experiments further validate the method's advantages. It outperforms existing methods in metrics such as mean Intersection over Union (mIoU) and Absolute Trajectory Error (ATE). Through ablation experiments, we revealed the synergistic effects between color, appearance, feature, and depth losses and the network structure, confirming the critical role of each component in improving positioning accuracy and semantic modeling performance. We also evaluated the impact of different loss weight combinations on model performance. By testing balanced, semantics-prioritized, and depth-prioritized weight combinations, we selected the optimal combination that effectively balances different loss components and improves overall model performance. Experiments on cross-attention weights temporal stability show that the model can maintain consistent performance in \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Different Weight Combinations**} & \multicolumn{2}{c}{**Replica**} & \multicolumn{2}{c}{**ScanNet**} \\ \cline{2-5}  & **mIoU \(\uparrow\)** & **ATE \(\downarrow\)** & **mIoU \(\uparrow\)** & **ATE \(\downarrow\)** \\ \hline Balance (\(\lambda_{r}=0.4\), \(\lambda_{s}=0.3\), \(\lambda_{d}=0.2\), \(\lambda_{f}=0.1\)) & 70.23 & 12.45 & 72.15 & 13.67 \\ sem-prior (\(\lambda_{r}=0.3\), \(\lambda_{s}=0.4\), \(\lambda_{d}=0.2\), \(\lambda_{f}=0.1\)) & 73.45 & 10.89 & 75.32 & 11.23 \\ depth-prior (\(\lambda_{r}=0.3\), \(\lambda_{s}=0.2\), \(\lambda_{d}=0.4\), \(\lambda_{f}=0.1\)) & **78.22** & **9.45** & **82.41** & **8.56** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of different loss weight combinations (the \(\downarrow\) means the smaller, the better, and the \(\uparrow\) means the larger, the better). dynamic environments. However, our method still faces limitations in certain scenarios, such as complex lighting, ultra-high-speed movement, and repetitive environments. These limitations may be due to the limitations of the dataset or the characteristics of the environment. In the future, we aim to overcome these limitations through further research and development. In conclusion, this study demonstrates excellent performance in camera pose estimation and semantic mapping tasks. To extend the system to larger and more unpredictable outdoor environments, future work will focus on exploring multi-sensor fusion strategies. Integrating an Inertial Measurement Unit (IMU) with visual sensors can compensate for vision limitations in rapid motion or occlusion scenarios. Meanwhile, incorporating millimeter-wave radar or LiDAR can enhance the system's robustness against illumination changes, adverse weather, and moving occlusions. Subsequent research will focus on validating the adaptability of this method under large-scale illumination variations, such as by employing multi-sensor fusion to address unpredictable interference factors. We believe these advancements will significantly enhance the system's practicality in complex outdoor environments. Conceptualization, Y.Z. ; methodology, S.L. ; software, C.Z. and Q.L. ; validation, J.H. and S.L. ; writing--original draft preparation, S.L. ; writing--review and editing, S.L. ; supervision, Y.Z. ; project administration, Y.Z. ; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.