## 5 Conclusions  
This paper presents a study on an unmanned aircraft system designed for dense 3D real-time reconstruction of urban pipelines using low-cost equipment. The research begins by building a UAV hardware platform for data acquisition, with the Intel RealSense D435 depth camera selected as the sensor for capturing RGB and depth information. The experimental scene focuses on a section of rubber sewage pipe, and three visual SLAM methods (RTAB-Map, ORB-SLAM2, and Manhattan-SLAM) are compared to evaluate their reconstruction effects. The results demonstrate that RTAB-Map outperforms the other two methods in terms of localization accuracy and 3D reconstruction. Additionally, RTAB-Map exhibits greater robustness in low-textured and dim urban pipe environments. The experiments confirm that the proposed UAV 3D reconstruction system, based on visual SLAM, enables real-time modeling in urban pipeline internal environments.  
The proposed UAV acquisition system for urban underground inner pipes offers several advantages: (i) UAVs are more lightweight and flexible in large-diameter scenarios such as urban sewage pipes, which reduces the risk of operational safety. (ii) RTAB-Map is better at real-time and reconstruction accuracy compared to other visual SLAM methods. (iii) RTAB-Map incorporates a memory management mechanism that enables real-time execution of data acquisition and modeling processes. This mechanism is crucial for ensuring pipeline stability analysis. (iv) Compared to Laser scanners, the depth camera used in this system is cost-effective while providing detailed texture information about the pipe in-walls.  
However, there are certain limitations in this study that need to be acknowledged. Firstly, in order to maintain the lightweight nature of the UAV, its range is limited and it cannot operate for extended periods of time. Secondly, the system faces difficulties in smooth metal pipes and other texture-deficient scenes, which is a drawback of the visual SLAM method. To overcome this limitation, the study will propose the fusion of LiDAR, IMU, and other sensors to enhance the accuracy of 3D reconstruction in the future. Lastly, the study only focuses on the performance of the system in straight pipe scenarios. Future research will delve into more complex situations involving underground pipes with multiple structures, such as curved pipes, vertical pipes, and 'T' shaped pipes.  
Conceptualization, X.C. and X.Z.; methodology, X.C. and X.Z.; software, X.Z. and C.L.; validation, X.Z. and C.L.; formal analysis, X.Z.; investigation, X.Z. and C.L.; resources, X.C. and X.Z.; data curation, C.L.; writing--original draft preparation, X.C. and X.Z.; writing--review and editing, X.C. All authors have read and agreed to the published version of the manuscript. This research was funded by China Postdoctoral Science Foundation (2017M622577), Hunan Provincial Natural Science Foundation (2018J2118). Not applicable. Not applicable. Not applicable. Not applicable. Data Availability StatementThe data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy restrictions.