## 1 Introduction  
Urban pipeline facilities play a vital role in energy transportation, clean water supply, and wastewater discharge in urban areas, thereby significantly improving the living conditions of urban residents. However, as pipes age, they may undergo degradation due to factors such as corrosion, geological subsidence, or improper plumbing and digging. This deterioration can lead to economic losses and hazardous incidents [1]. Therefore, obtaining internal pipe information is crucial for the sustainable operation of urban pipelines as it provides early support for pipeline inspection and maintenance. Traditional pipe inspection methods depend on manual work and are limited by the following factors: (i) they are time consuming and laborious; (ii) they risk lives if entering into unknown pipe environments. Therefore, robotic solutions for pipeline inspection promise enhancement of human labor by automating data acquisition for pipe condition assessments. CCTV (closed-circuit television) cameras or sonar devices are some of the most well-adopted robotic solutions used to obtain information about the interior of pipelines for basic visual inspection as well as data collection and video analysis [1]. Although CCTV is the preferred method because it provides a complete view of the pipe interior, it requires long cables and travels with slow speed in small-diameter pipeline inspections. To gain further insight into the 3D structure and textures of the pipeline, additional steps must be taken with the video data or image sequences to achieve 3D reconstruction. This is often accomplished offline by employing the SFM method [2]. Additionally, multi-sensor fusion SLAM techniques from the robotics community can be utilized to automatically collect sensory data and perform RGB-D 3D reconstructions within the pipes. [PERSON] and [PERSON] proposed a pipeline reconstruction system using a multi-camera array, which includes one centrally positioned depth camera and four oblique depth cameras. This system estimates the pipeline path and maps its surface based on the vision SLAM framework. However, it should be noted that this method is not capable of real-time reconstruction, and the use of multiple cameras increases the integrated cost of the system [3]. In another study, [PERSON] et al. presented a pipeline reconstruction SLAM method that employs a cylindrical rule. This method addresses the issues of scale drift and accuracy reduction encountered in the vision SLAM approach for pipeline reconstruction. However, it is important to mention that the accuracy of this method significantly decreases when the lower part of the pipeline is covered [4]. [PERSON] et al. also introduced a 3D reconstruction system based on 360\({}^{\circ}\) panoramic video. This system involves multi-view image frame extraction, panoramic reprojection, and photogrammetric processing. It provides an intuitive and clear reconstruction of the pipeline's real scenes, but it can only be operated offline [5]. While these approaches offer valuable information, including visual, 3D, and geo-referencing data, for advanced pipe condition monitoring and assessment, they are time-consuming and require offline operation. Consequently, there is a need for autonomous, unconstrained robots that can perform continuous, real-time pipeline health monitoring [4]. In recent years, SLAM technology has gained wide popularity in solving complex problems related to real-time 3D mapping in robots. Several improved algorithms based on SLAM have been developed to enhance work efficiency. The ORB-SLAM2 method, for instance, requires ORB features with better view invariance and higher matching efficiency. However, it is susceptible to tracking failures in scenes with fast camera movement and weak textures. Additionally, the maps generated by ORB-SLAM2 are sparse point cloud maps with limited applicability [6]. [PERSON] et al.