Learning and SLAM Based Decision Support Platform for Sewer Inspection

Abstract

Routine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing systems merely rely on video records for visual examination since sensors such as a laser scanner or sonar are costly, and the data processing requires expertise. This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion. As the platform moving, the g-sensor reflects the pipeline flatness, while an integrated simultaneous localization and mapping (SLAM) strategy reconstructs the 3D map of the pipeline conditions simultaneously. In the light of the experimental results, the reconstructed moving trajectory achieved a relative accuracy of 0.016 m when no additional control points deployed along the inspecting path. The geometric information of observed defects accomplishes an accuracy of 0.9 cm in length and width estimation and an accuracy of 1.1% in block ratio evaluation, showing promising results for practical sewer inspection. Moreover, the labeled deficiencies directly increase the automation level of documenting irregularity and facilitate the understanding of pipeline conditions for management and maintenance.

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

“…In [139] structured lighting was used to recover the scale factor in monocular VO by minimising the reprojection error of two laser spots [139]. Distinct from both stereo and monocular systems are depth cameras, which have the advantage that they can also be readily used to detect obstacles in the pipe [203].…”

Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review

“…Sequential images acquired from the camera are used to measure travel distance and to construct the global image of the internal surface. In [54], the researchers proposed a method for fault detection in a sewer pipe with data fusion of depth camera, infrared, and g-sensor and built the 3D map of the pipeline with SLAM. The authors in [48] considered the SLAM problem for metal water pipes.…”

“…Navigation [45].  Pipeline Flatness Detection [54].  Simultaneous Localization and Mapping (SLAM) [56].…”

Editorial for the Special Issue on Selected Papers from the “2019 International Symposium on Remote Sensing”

“…Machine learning-based algorithms have been rapidly adopted and adapted for remote sensing image processing and analysis, applied not only to conventional satellite and airborne images but also to data collected with mobile mapping systems and drones. Among the selected publications, Chuang and Sung [6] developed a mobile mapping platform that consists of low-cost infrared and depth cameras with micromachined microelectromechanical systems (MEMS) g-sensors (accelerometers) to support sewer pipeline inspection applications. The developed mobile mapping platform utilized the image POSE and simultaneous localization and mapping (SLAM) techniques to reconstruct 3D point clouds in indoor environments.…”