Sewer Sediment Inspection Based on Multisensor Fusion Considering Sewage Flow

Chen Li (), Ke Chen (), Hanlin Li (), Yixiao Shao and Hanbin Luo ()
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Chen Li: Huazhong University of Science and Technology
Ke Chen: Huazhong University of Science and Technology
Hanlin Li: Huazhong University of Science and Technology
Yixiao Shao: Huazhong University of Science and Technology
Hanbin Luo: Huazhong University of Science and Technology

A chapter in Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate, 2023, pp 431-439 from Springer

Abstract: Abstract Sewers are essential infrastructure for transporting urban wastewater, but their operational efficiency can be compromised by sedimentation. Unfortunately, CCTV (closed-circuit television) and advanced sewer sediment inspection technologies fail to quantify the volume of sediment and accurately locate the sediment distribution. Furthermore, most of the existing technologies are ineffective in a fully loaded sewer. This research presents a multisensor data fusion method for sewer sediment inspection by an autonomous underwater vehicle (AUV). An AUV is equipped with a rotating sonar device, a gyroscope, an accelerometer, and an odometer. Multisensor data fusion is divided into two steps. In the first step, the Kalman filtering technique is applied to fuse gyroscope, accelerometer, and odometer data to solve the problem of AUV localization in sewers with weak or no signal. In the second step, the point cloud data collected by sonar are fused with the fused sensor data to solve the point cloud data offset problem caused by the current and AUV motion. Overall, the proposed method has great practical potential for accurate sediment inspection in each section of a sewer, which can be used to support decision-making in urban sewer maintenance.

Keywords: Sewer sediment inspection; Autonomous underwater vehicle; Multisensor data fusion; Kalman filtering; Point cloud data (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-3626-7_34

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