## 1 Introduction
proposed a robust RGB-D SLAM system that utilizes point and line features to improve robustness in low-texture scenes, which are more sensitive to light variations [7]. On the other hand, LIO-SLAM achieves relatively high localization and map construction accuracy by fusing LiDAR data and IMUs. However, it comes with high costs and is typically used for generating 2D maps, which may not suffice for tasks requiring 3D environment modeling [8]. DSO-SLAM, which employs the sparse direct method, extracts feature points directly from the image without the need for dense feature matching. It is capable of functioning in environments with changing illumination and weak texture, making it more robust [9]. VINS-Fusion, on the other hand, utilizes a vision camera and IMU fusion technology, offering high positioning accuracy, real-time capabilities, and stability in both indoor and outdoor scenes. However, it requires precise calibration of sensors, including camera-IMU time synchronization and external parameter calibration. Moreover, it is primarily designed for high-precision positioning applications and is not suitable for 3D map construction [10]. Manhattan SLAM is a technology integrating superpixels and Manhattan world assumptions, in which both line features and planar features can be better extracted; however, it cannot be used for the 3D reconstruction of pipelines [11]. Notably, the performance of different improved SLAM algorithms can significantly differ in diverse application environments. Consequently, when applying SLAM to different contexts, it becomes crucial to select the most suitable SLAM approach or adapt existing methods to balance efficiency and accuracy. Although various methods and systems have made significant progress in achieving optimal results, there is still a demand for automated and real-time 3D reconstruction of underground pipelines in complex environments. For instance, the robotic vehicle inspection system equipped with CCTV has limitations when it comes to inspecting large-diameter or irregular-shaped pipelines. Additionally, these systems often require manual operators and lack autonomy. Existing SLAM studies primarily focus on improving reconstruction accuracy, while efficiency and real-time performance receive less attention. [PERSON] et al. propose an end-to-end network called SVR-Net for monocular TSDF SLAM, which directly generates dense TSDF maps during localization, avoids inconsistencies in depth map fusion, and meets real-time requirements [12]. However, this approach requires pre-training and re-execution in different application scenarios. Deep learning has proven to be effective in image processing, and many works have applied it to front-end feature matching and loopback detection in visual SLAM, reducing the false matching rate. However, it relies on strong arithmetic support and prior information. It is particularly important to use a low arithmetic platform that is easy to operate without the need for training or introducing prior information. RTAB-Map is a representative method for RGB-D SLAM, which generates point clouds and triangular mesh maps [13]. Its binary program is available in the ROS system due to its high integration. It can run in real time on low-computing power platforms and generates dense point cloud maps. The binary program of RTAB-Map can optimize both bit-pose filtering and model reconstruction of the point cloud data. Nowadays, UAVs have gained significant importance in structural health performance inspections due to their small size, low cost, flexibility, mobility, and ability to perform multiple-view inspections [14; 15; 16; 17]. However, their application in pipeline inspection missions has been limited to the outer surface of pipes and they have not yet been deployed for 3D reconstruction of the interior pipeline [18; 19; 20]. This limitation is primarily attributed to the vast and complex nature of pipeline systems with narrow spaces and dim lighting conditions.