3D Sensors for Sewer Inspection: A Quantitative Review and Analysis

Automating inspection of critical infrastructure such as sewer systems will help utilities optimize maintenance and replacement schedules. The current inspection process consists of manual reviews of video as an operator controls a sewer inspection vehicle remotely. The process is slow, labor-intens...

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Main Authors: Chris H. Bahnsen, Anders S. Johansen, Mark P. Philipsen, Jesper W. Henriksen, Kamal Nasrollahi, Thomas B. Moeslund
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/21/7/2553
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spelling doaj-1b855a1a4c9d456e94e85bac70f18ff12021-04-06T23:02:10ZengMDPI AGSensors1424-82202021-04-01212553255310.3390/s210725533D Sensors for Sewer Inspection: A Quantitative Review and AnalysisChris H. Bahnsen0Anders S. Johansen1Mark P. Philipsen2Jesper W. Henriksen3Kamal Nasrollahi4Thomas B. Moeslund5Visual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkVisual Analysis and Perception (VAP) Lab, Aalborg University, Rendsburggade 14, 9000 Aalborg, DenmarkAutomating inspection of critical infrastructure such as sewer systems will help utilities optimize maintenance and replacement schedules. The current inspection process consists of manual reviews of video as an operator controls a sewer inspection vehicle remotely. The process is slow, labor-intensive, and expensive and presents a huge potential for automation. With this work, we address a central component of the next generation of robotic inspection of sewers, namely the choice of 3D sensing technology. We investigate three prominent techniques for 3D vision: passive stereo, active stereo, and time-of-flight (ToF). The Realsense D435 camera is chosen as the representative of the first two techniques wheres the PMD CamBoard pico flexx represents ToF. The 3D reconstruction performance of the sensors is assessed in both a laboratory setup and in an outdoor above-ground setup. The acquired point clouds from the sensors are compared with reference 3D models using the cloud-to-mesh metric. The reconstruction performance of the sensors is tested with respect to different illuminance levels and different levels of water in the pipes. The results of the tests show that the ToF-based point cloud from the pico flexx is superior to the output of the active and passive stereo cameras.https://www.mdpi.com/1424-8220/21/7/2553sewer inspectionsewer pipes3D vision3D reconstructioncomputer visionautomated inspection
collection DOAJ
language English
format Article
sources DOAJ
author Chris H. Bahnsen
Anders S. Johansen
Mark P. Philipsen
Jesper W. Henriksen
Kamal Nasrollahi
Thomas B. Moeslund
spellingShingle Chris H. Bahnsen
Anders S. Johansen
Mark P. Philipsen
Jesper W. Henriksen
Kamal Nasrollahi
Thomas B. Moeslund
3D Sensors for Sewer Inspection: A Quantitative Review and Analysis
Sensors
sewer inspection
sewer pipes
3D vision
3D reconstruction
computer vision
automated inspection
author_facet Chris H. Bahnsen
Anders S. Johansen
Mark P. Philipsen
Jesper W. Henriksen
Kamal Nasrollahi
Thomas B. Moeslund
author_sort Chris H. Bahnsen
title 3D Sensors for Sewer Inspection: A Quantitative Review and Analysis
title_short 3D Sensors for Sewer Inspection: A Quantitative Review and Analysis
title_full 3D Sensors for Sewer Inspection: A Quantitative Review and Analysis
title_fullStr 3D Sensors for Sewer Inspection: A Quantitative Review and Analysis
title_full_unstemmed 3D Sensors for Sewer Inspection: A Quantitative Review and Analysis
title_sort 3d sensors for sewer inspection: a quantitative review and analysis
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2021-04-01
description Automating inspection of critical infrastructure such as sewer systems will help utilities optimize maintenance and replacement schedules. The current inspection process consists of manual reviews of video as an operator controls a sewer inspection vehicle remotely. The process is slow, labor-intensive, and expensive and presents a huge potential for automation. With this work, we address a central component of the next generation of robotic inspection of sewers, namely the choice of 3D sensing technology. We investigate three prominent techniques for 3D vision: passive stereo, active stereo, and time-of-flight (ToF). The Realsense D435 camera is chosen as the representative of the first two techniques wheres the PMD CamBoard pico flexx represents ToF. The 3D reconstruction performance of the sensors is assessed in both a laboratory setup and in an outdoor above-ground setup. The acquired point clouds from the sensors are compared with reference 3D models using the cloud-to-mesh metric. The reconstruction performance of the sensors is tested with respect to different illuminance levels and different levels of water in the pipes. The results of the tests show that the ToF-based point cloud from the pico flexx is superior to the output of the active and passive stereo cameras.
topic sewer inspection
sewer pipes
3D vision
3D reconstruction
computer vision
automated inspection
url https://www.mdpi.com/1424-8220/21/7/2553
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