## 6 Conclusions  
A novel RGB-D SLAM approach that uses priori LiDAR point cloud data as the guidance for indoor 3-D scene construction and navigation was presented in this paper. There were three important innovations in this method: first, the high-precision geometrical control information from the LiDAR data of the indoor scene was used to eliminate the cumulative error of the RGB-D SLAM system in large-scale indoor operation; second, a combination of deep learning and geometric SLAM system enabled automatic initialization and motion recovery, which improved the robustness of the system in challenging environments; and, third, the multithreaded map segment optimization method using double window was adopted to ensure the algorithm runs in real-time with high precision under the guidance of LiDAR data and constructed large-scale accurate and consistent dense map, which can patch the LiDAR data holes. The standard deviation of the 3D map construction was approximately 10 cm in a travel distance of 100 m compared with the LiDAR ground truth, and the relative cumulative error of the camera in closed-loop experiments was 0.09%, which was twice less than that of the typical SLAM algorithm (3.4%). Thus, it is clear that the positioning accuracy and map construction accuracy of the proposed system are significantly higher than those of the most advanced SLAM systems that do not utilize priori data. Furthermore, the fusion of the 2-D rich texture image data from RGB-D camera and the high-precision 3-D terrestrial LiDAR point cloud data was performed automatically; it compensated the shortcomings of the LiDAR data, which has high precision but insufficient texture information. Moreover, the proposed method can effectively construct dense indoor maps of large indoor scenes under the unified coordinate system of multiple data sources, for scene recognition and understanding, robot path planning and navigation, artificial intelligence (e.g., robot and 3D environment interaction), augmented reality, and virtual game environment development.  
In future, we will build semantic maps of large scenes based on the constraints of terrestrial LiDAR data. Accurate poses obtained from terrestrial LiDAR data assist accurate semantic segmentation of single-frame images and enhance the consistency of semantic segmentation of multiview image frames so that robots can interact with the environment better.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operation** & **Medium (ms)** & **Mean (ms)** & **Std. Dev. (ms)** \\ \hline LiDAR optimization & 44.92 & 40.20 & 11.26 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Average optimization time per keyframe in LiDAR correction thread.  
**Author Contributions:** Conceptualization: J.L.; methodology: J.L. and X.K.; software: X.K., J.L., and W.W.; validation: X.K.; formal analysis, X.K.; investigation, X.K. and J.L.; resources, J.L. and X.F.; data curation, J.L. and X.K.; writing--J.L. and X.K.; writing--review and editing, J.L.; visualization, X.K.; supervision, J.L. and X.F.; project administration, J.L. and X.F.; funding acquisition, J.L. and X.F.  
**Funding:** This work was funded and supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19080101). This work was also partially supported by Key Deployment Program of Chinese Academy of Sciences (No. KFZD-SW-317), the National Key Research and Development Program of China (No. 2016 YFB0502102), and the National Natural Science Foundation of China (No. 41101436).  
**Conflicts of Interest:** The authors declare no conflict of interest.